Introduction to Perf
17 likes3,158 views
Perf is a Linux profiler tool that uses performance monitoring hardware to count various events like CPU cycles, instructions, and cache misses. It can count events for a single thread, entire process, specific CPUs, or system-wide. Perf stat is used to count events during process execution, while perf record collects profiling data in a file for later analysis with perf report.
1 of 13
Downloaded 67 times
Ad
Recommended
Kernel Recipes 2017: Using Linux perf at Netflix
Kernel Recipes 2017: Using Linux perf at NetflixBrendan Gregg This document discusses using the Linux perf profiling tool at Netflix. It begins with an overview of why Netflix needs Linux profiling to understand CPU usage quickly and completely. It then provides an introduction to the perf tool, covering its basic workflow and commands. The document discusses profiling CPU usage with perf, including potential issues like JIT runtimes and missing symbols. It provides several examples of perf commands for listing, counting, and recording events. The overall summary is that perf allows Netflix to quickly and accurately profile CPU usage across the entire software stack, from applications to libraries to the kernel, to optimize performance.
Linux Performance Profiling and Monitoring
Linux Performance Profiling and MonitoringGeorg Schönberger Presentation about Linux Performance Profiling and Monitoring, held at OSDC 2015 - a conference powered by NETWAYS.
Profiling your Applications using the Linux Perf Tools
Profiling your Applications using the Linux Perf ToolsemBO_Conference This document provides an overview of using the Linux perf tools to profile applications. It discusses setting up perf, benchmarking applications, profiling both CPU usage and sleep times, and analyzing profiling data. The document covers perf commands like perf record to collect profiling data, perf report to analyze the data, and perf script to convert it to other formats. It also discusses profiling options like call graphs and collecting kernel vs. user mode events.
Performance Wins with eBPF: Getting Started (2021)
Performance Wins with eBPF: Getting Started (2021)Brendan Gregg This document provides an overview of using eBPF (extended Berkeley Packet Filter) to quickly get performance wins as a sysadmin. It recommends installing BCC and bpftrace tools to easily find issues like periodic processes, misconfigurations, unexpected TCP sessions, or slow file system I/O. A case study examines using biosnoop to identify which processes were causing disk latency issues. The document suggests thinking like a sysadmin first by running tools, then like a programmer if a problem requires new tools. It also outlines recommended frontends depending on use cases and provides references to learn more about BPF.
Performance Analysis Tools for Linux Kernel
Performance Analysis Tools for Linux Kernellcplcp1 Perf is a collection of Linux kernel tools for performance monitoring and profiling. It provides sampling and profiling of the system to analyze performance bottlenecks. Perf supports hardware events from the CPU performance counters, software events from the kernel, and tracepoint events from the kernel and loaded modules. It offers tools like perf record to sample events and store them, perf report to analyze stored samples, and perf trace to trace system events in real-time.
The linux networking architecture
The linux networking architecturehugo lu The document discusses Linux networking architecture and covers several key topics in 3 paragraphs or less:
It first describes the basic structure and layers of the Linux networking stack including the network device interface, network layer protocols like IP, transport layer, and sockets. It then discusses how network packets are managed in Linux through the use of socket buffers and associated functions. The document also provides an overview of the data link layer and protocols like Ethernet, PPP, and how they are implemented in Linux.
Systems@Scale 2021 BPF Performance Getting Started
Systems@Scale 2021 BPF Performance Getting StartedBrendan Gregg Talk for Facebook Systems@Scale 2021 by Brendan Gregg: "BPF (eBPF) tracing is the superpower that can analyze everything, helping you find performance wins, troubleshoot software, and more. But with many different front-ends and languages, and years of evolution, finding the right starting point can be hard. This talk will make it easy, showing how to install and run selected BPF tools in the bcc and bpftrace open source projects for some quick wins. Think like a sysadmin, not like a programmer."
eBPF Perf Tools 2019
eBPF Perf Tools 2019Brendan Gregg The document describes a biolatency tool that traces block device I/O latency using eBPF. It discusses how the tool was originally written in the bcc framework using C/BPF, but has since been rewritten in the bpftrace framework using a simpler one-liner script. It provides examples of the bcc and bpftrace implementations of biolatency.
Tracing MariaDB server with bpftrace - MariaDB Server Fest 2021
Tracing MariaDB server with bpftrace - MariaDB Server Fest 2021Valeriy Kravchuk Bpftrace is a relatively new eBPF-based open source tracer for modern Linux versions (kernels 5.x.y) that is useful for analyzing production performance problems and troubleshooting software. Basic usage of the tool, as well as bpftrace one liners and advanced scripts useful for MariaDB DBAs are presented. Problems of MariaDB Server dynamic tracing with bpftrace and some possible solutions and alternative tracing tools are discussed.
Performance Wins with BPF: Getting Started
Performance Wins with BPF: Getting StartedBrendan Gregg Keynote by Brendan Gregg for the eBPF summit, 2020. How to get started finding performance wins using the BPF (eBPF) technology. This short talk covers the quickest and easiest way to find performance wins using BPF observability tools on Linux.
The TCP/IP Stack in the Linux Kernel
The TCP/IP Stack in the Linux KernelDivye Kapoor The document discusses various data structures and functions related to network packet processing in the Linux kernel socket layer. It describes the sk_buff structure that is used to pass packets between layers. It also explains the net_device structure that represents a network interface in the kernel. When a packet is received, the interrupt handler will raise a soft IRQ for processing. The packet will then traverse various protocol layers like IP and TCP to be eventually delivered to a socket and read by a userspace application.
Linux Systems Performance 2016
Linux Systems Performance 2016Brendan Gregg Talk for PerconaLive 2016 by Brendan Gregg. Video: https://www.youtube.com/watch?v=CbmEDXq7es0 . "Systems performance provides a different perspective for analysis and tuning, and can help you find performance wins for your databases, applications, and the kernel. However, most of us are not performance or kernel engineers, and have limited time to study this topic. This talk summarizes six important areas of Linux systems performance in 50 minutes: observability tools, methodologies, benchmarking, profiling, tracing, and tuning. Included are recipes for Linux performance analysis and tuning (using vmstat, mpstat, iostat, etc), overviews of complex areas including profiling (perf_events), static tracing (tracepoints), and dynamic tracing (kprobes, uprobes), and much advice about what is and isn't important to learn. This talk is aimed at everyone: DBAs, developers, operations, etc, and in any environment running Linux, bare-metal or the cloud."
Linux Profiling at Netflix
Linux Profiling at NetflixBrendan Gregg This talk discusses Linux profiling using perf_events (also called "perf") based on Netflix's use of it. It covers how to use perf to get CPU profiling working and overcome common issues. The speaker will give a tour of perf_events features and show how Netflix uses it to analyze performance across their massive Amazon EC2 Linux cloud. They rely on tools like perf for customer satisfaction, cost optimization, and developing open source tools like NetflixOSS. Key aspects covered include why profiling is needed, a crash course on perf, CPU profiling workflows, and common "gotchas" to address like missing stacks, symbols, or profiling certain languages and events.
Understand and optimize Linux I/O
Understand and optimize Linux I/OAndrea Righi This document summarizes an Italian presentation on monitoring and tuning I/O performance on Linux. It discusses key topics like I/O monitoring tools like iostat, iotop and blktrace, tuning techniques like dirty page writeback and filesystem options, and ensuring reliability of data writes through proper synchronization and error handling. The presentation provides an overview of I/O subsystems in Linux and dives into specific tools and parameters for optimizing I/O.
eBPF maps 101
eBPF maps 101SUSE Labs Taipei The document provides an overview of eBPF maps and how they can be used to share data between eBPF programs running in the kernel and userspace applications. It describes how maps are created via the BPF syscall using the BPF_MAP_CREATE command. It also explains how keys and values can be looked up, updated, and deleted from maps using commands like BPF_MAP_LOOKUP_ELEM, BPF_MAP_UPDATE_ELEM, and BPF_MAP_DELETE_ELEM. Finally, it lists the different types of eBPF maps available.
Tuning Android for low RAM
Tuning Android for low RAMChris Simmonds Slides from Android Builder's Summit 2014 in San Jose, CA
The 4.4 KitKat release includes the results of “Project Svelte”: a set of tweaks to the operating system to make it run more easily on devices with around 512 MiB RAM. This is likely to be especially important for people working with “Embedded Android”, that is, implementing Android on devices that are not smart phones or tablets.
Linux 4.x Tracing Tools: Using BPF Superpowers
Linux 4.x Tracing Tools: Using BPF SuperpowersBrendan Gregg Talk for USENIX LISA 2016 by Brendan Gregg.
"Linux 4.x Tracing Tools: Using BPF Superpowers
The Linux 4.x series heralds a new era of Linux performance analysis, with the long-awaited integration of a programmable tracer: Enhanced BPF (eBPF). Formally the Berkeley Packet Filter, BPF has been enhanced in Linux to provide system tracing capabilities, and integrates with dynamic tracing (kprobes and uprobes) and static tracing (tracepoints and USDT). This has allowed dozens of new observability tools to be developed so far: for example, measuring latency distributions for file system I/O and run queue latency, printing details of storage device I/O and TCP retransmits, investigating blocked stack traces and memory leaks, and a whole lot more. These lead to performance wins large and small, especially when instrumenting areas that previously had zero visibility. Tracing superpowers have finally arrived.
In this talk I'll show you how to use BPF in the Linux 4.x series, and I'll summarize the different tools and front ends available, with a focus on iovisor bcc. bcc is an open source project to provide a Python front end for BPF, and comes with dozens of new observability tools (many of which I developed). These tools include new BPF versions of old classics, and many new tools, including: execsnoop, opensnoop, funccount, trace, biosnoop, bitesize, ext4slower, ext4dist, tcpconnect, tcpretrans, runqlat, offcputime, offwaketime, and many more. I'll also summarize use cases and some long-standing issues that can now be solved, and how we are using these capabilities at Netflix."
Understanding of linux kernel memory model
Understanding of linux kernel memory modelSeongJae Park SeongJae Park introduces himself and his work contributing to the Linux kernel memory model documentation. He developed a guaranteed contiguous memory allocator and maintains the Korean translation of the kernel's memory barrier documentation. The document discusses how the increasing prevalence of multi-core processors requires careful programming to ensure correct parallel execution given relaxed memory ordering. It notes that compilers and CPUs optimize for instruction throughput over programmer goals, and memory accesses can be reordered in ways that affect correctness on multi-processors. Understanding the memory model is important for writing high-performance parallel code.
BPF: Tracing and more
BPF: Tracing and moreBrendan Gregg Video: https://www.youtube.com/watch?v=JRFNIKUROPE . Talk for linux.conf.au 2017 (LCA2017) by Brendan Gregg, about Linux enhanced BPF (eBPF). Abstract:
A world of new capabilities is emerging for the Linux 4.x series, thanks to enhancements that have been included in Linux for to Berkeley Packet Filter (BPF): an in-kernel virtual machine that can execute user space-defined programs. It is finding uses for security auditing and enforcement, enhancing networking (including eXpress Data Path), and performance observability and troubleshooting. Many new open source tools that have been written in the past 12 months for performance analysis that use BPF. Tracing superpowers have finally arrived for Linux!
For its use with tracing, BPF provides the programmable capabilities to the existing tracing frameworks: kprobes, uprobes, and tracepoints. In particular, BPF allows timestamps to be recorded and compared from custom events, allowing latency to be studied in many new places: kernel and application internals. It also allows data to be efficiently summarized in-kernel, including as histograms. This has allowed dozens of new observability tools to be developed so far, including measuring latency distributions for file system I/O and run queue latency, printing details of storage device I/O and TCP retransmits, investigating blocked stack traces and memory leaks, and a whole lot more.
This talk will summarize BPF capabilities and use cases so far, and then focus on its use to enhance Linux tracing, especially with the open source bcc collection. bcc includes BPF versions of old classics, and many new tools, including execsnoop, opensnoop, funcccount, ext4slower, and more (many of which I developed). Perhaps you'd like to develop new tools, or use the existing tools to find performance wins large and small, especially when instrumenting areas that previously had zero visibility. I'll also summarize how we intend to use these new capabilities to enhance systems analysis at Netflix.
Embedded Linux Kernel - Build your custom kernel
Embedded Linux Kernel - Build your custom kernelEmertxe Information Technologies Pvt Ltd Build your own Embedded Linux Kernel by understanding it source code organization, compilation ecosystem and compiling it for a custom target.
New Ways to Find Latency in Linux Using Tracing
New Ways to Find Latency in Linux Using TracingScyllaDB Ftrace is the official tracer of the Linux kernel. It originated from the real-time patch (now known as PREEMPT_RT), as developing an operating system for real-time use requires deep insight and transparency of the happenings of the kernel. Not only was tracing useful for debugging, but it was critical for finding areas in the kernel that was causing unbounded latency. It's no wonder why the ftrace infrastructure has a lot of tooling for seeking out latency. Ftrace was introduced into mainline Linux in 2008, and several talks have been done on how to utilize its tracing features. But a lot has happened in the past few years that makes the tooling for finding latency much simpler. Other talks at P99 will discuss the new ftrace tracers "osnoise" and "timerlat", but this talk will focus more on the new flexible and dynamic aspects of ftrace that facilitates finding latency issues which are more specific to your needs. Some of this work may still be in a proof of concept stage, but this talk will give you the advantage of knowing what tools will be available to you in the coming year.
USENIX ATC 2017: Visualizing Performance with Flame Graphs
USENIX ATC 2017: Visualizing Performance with Flame GraphsBrendan Gregg Talk by Brendan Gregg for USENIX ATC 2017.
"Flame graphs are a simple stack trace visualization that helps answer an everyday problem: how is software consuming resources, especially CPUs, and how did this change since the last software version? Flame graphs have been adopted by many languages, products, and companies, including Netflix, and have become a standard tool for performance analysis. They were published in "The Flame Graph" article in the June 2016 issue of Communications of the ACM, by their creator, Brendan Gregg.
This talk describes the background for this work, and the challenges encountered when profiling stack traces and resolving symbols for different languages, including for just-in-time compiler runtimes. Instructions will be included generating mixed-mode flame graphs on Linux, and examples from our use at Netflix with Java. Advanced flame graph types will be described, including differential, off-CPU, chain graphs, memory, and TCP events. Finally, future work and unsolved problems in this area will be discussed."
Meet cute-between-ebpf-and-tracing
Meet cute-between-ebpf-and-tracingViller Hsiao This document discusses how eBPF (extended Berkeley Packet Filter) can be used for kernel tracing. It provides an overview of BPF and eBPF, how eBPF programs are compiled and run in the kernel, the use of BPF maps, and how eBPF enables new possibilities for dynamic kernel instrumentation through techniques like Kprobes and ftrace.
Understanding eBPF in a Hurry!
Understanding eBPF in a Hurry!Ray Jenkins eBPF is an exciting new technology that is poised to transform Linux performance engineering. eBPF enables users to dynamically and programatically trace any kernel or user space code path, safely and efficiently. However, understanding eBPF is not so simple. The goal of this talk is to give audiences a fundamental understanding of eBPF, how it interconnects existing Linux tracing technologies, and provides a powerful aplatform to solve any Linux performance problem.
BPF - in-kernel virtual machine
BPF - in-kernel virtual machineAlexei Starovoitov BPF of Berkeley Packet Filter mechanism was first introduced in linux in 1997 in version 2.1.75. It has seen a number of extensions of the years. Recently in versions 3.15 - 3.19 it received a major overhaul which drastically expanded it's applicability. This talk will cover how the instruction set looks today and why. It's architecture, capabilities, interface, just-in-time compilers. We will also talk about how it's being used in different areas of the kernel like tracing and networking and future plans.
Linux Performance Analysis: New Tools and Old Secrets
Linux Performance Analysis: New Tools and Old SecretsBrendan Gregg Talk for USENIX/LISA2014 by Brendan Gregg, Netflix. At Netflix performance is crucial, and we use many high to low level tools to analyze our stack in different ways. In this talk, I will introduce new system observability tools we are using at Netflix, which I've ported from my DTraceToolkit, and are intended for our Linux 3.2 cloud instances. These show that Linux can do more than you may think, by using creative hacks and workarounds with existing kernel features (ftrace, perf_events). While these are solving issues on current versions of Linux, I'll also briefly summarize the future in this space: eBPF, ktap, SystemTap, sysdig, etc.
![[232] 성능어디까지쥐어짜봤니 송태웅](https://cdn.slidesharecdn.com/ss_thumbnails/232-161025013504-thumbnail.jpg?width=560&fit=bounds)
UM2019 Extended BPF: A New Type of Software
UM2019 Extended BPF: A New Type of SoftwareBrendan Gregg BPF (Berkeley Packet Filter) has evolved from a limited virtual machine for efficient packet filtering to a new type of software called extended BPF. Extended BPF allows for custom, efficient, and production-safe performance analysis tools and observability programs to be run in the Linux kernel through BPF. It enables new event-based applications running as BPF programs attached to various kernel events like kprobes, uprobes, tracepoints, sockets, and more. Major companies like Facebook, Google, and Netflix are using BPF programs for tasks like intrusion detection, container security, firewalling, and observability with over 150,000 AWS instances running BPF programs. BPF provides a new program model and security features compared
Always-on Profiling of All Linux Threads, On-CPU and Off-CPU, with eBPF & Con...
Always-on Profiling of All Linux Threads, On-CPU and Off-CPU, with eBPF & Con...ScyllaDB In this session, Tanel introduces a new open source eBPF tool for efficiently sampling both on-CPU events and off-CPU events for every thread (task) in the OS. Linux standard performance tools (like perf) allow you to easily profile on-CPU threads doing work, but if we want to include the off-CPU timing and reasons for the full picture, things get complicated. Combining eBPF task state arrays with periodic sampling for profiling allows us to get both a system-level overview of where threads spend their time, even when blocked and sleeping, and allow us to drill down into individual thread level, to understand why.
Kernel Recipes 2017 - Using Linux perf at Netflix - Brendan Gregg
Kernel Recipes 2017 - Using Linux perf at Netflix - Brendan GreggAnne Nicolas
Linux perf is a crucial performance analysis tool at Netflix, and is used by a self-service GUI for generating CPU flame graphs and other reports. This sounds like an easy task, however, getting perf to work properly in VM guests running Java, Node.js, containers, and other software, has been at times a challenge. This talk summarizes Linux perf, how we use it at Netflix, the various gotchas we have encountered, and a summary of advanced features.
Brendan Gregg, Netflix
Ad
More Related Content
What's hot (20)
Tracing MariaDB server with bpftrace - MariaDB Server Fest 2021
Tracing MariaDB server with bpftrace - MariaDB Server Fest 2021Valeriy Kravchuk Bpftrace is a relatively new eBPF-based open source tracer for modern Linux versions (kernels 5.x.y) that is useful for analyzing production performance problems and troubleshooting software. Basic usage of the tool, as well as bpftrace one liners and advanced scripts useful for MariaDB DBAs are presented. Problems of MariaDB Server dynamic tracing with bpftrace and some possible solutions and alternative tracing tools are discussed.
Performance Wins with BPF: Getting Started
Performance Wins with BPF: Getting StartedBrendan Gregg Keynote by Brendan Gregg for the eBPF summit, 2020. How to get started finding performance wins using the BPF (eBPF) technology. This short talk covers the quickest and easiest way to find performance wins using BPF observability tools on Linux.
The TCP/IP Stack in the Linux Kernel
The TCP/IP Stack in the Linux KernelDivye Kapoor The document discusses various data structures and functions related to network packet processing in the Linux kernel socket layer. It describes the sk_buff structure that is used to pass packets between layers. It also explains the net_device structure that represents a network interface in the kernel. When a packet is received, the interrupt handler will raise a soft IRQ for processing. The packet will then traverse various protocol layers like IP and TCP to be eventually delivered to a socket and read by a userspace application.
Linux Systems Performance 2016
Linux Systems Performance 2016Brendan Gregg Talk for PerconaLive 2016 by Brendan Gregg. Video: https://www.youtube.com/watch?v=CbmEDXq7es0 . "Systems performance provides a different perspective for analysis and tuning, and can help you find performance wins for your databases, applications, and the kernel. However, most of us are not performance or kernel engineers, and have limited time to study this topic. This talk summarizes six important areas of Linux systems performance in 50 minutes: observability tools, methodologies, benchmarking, profiling, tracing, and tuning. Included are recipes for Linux performance analysis and tuning (using vmstat, mpstat, iostat, etc), overviews of complex areas including profiling (perf_events), static tracing (tracepoints), and dynamic tracing (kprobes, uprobes), and much advice about what is and isn't important to learn. This talk is aimed at everyone: DBAs, developers, operations, etc, and in any environment running Linux, bare-metal or the cloud."
Linux Profiling at Netflix
Linux Profiling at NetflixBrendan Gregg This talk discusses Linux profiling using perf_events (also called "perf") based on Netflix's use of it. It covers how to use perf to get CPU profiling working and overcome common issues. The speaker will give a tour of perf_events features and show how Netflix uses it to analyze performance across their massive Amazon EC2 Linux cloud. They rely on tools like perf for customer satisfaction, cost optimization, and developing open source tools like NetflixOSS. Key aspects covered include why profiling is needed, a crash course on perf, CPU profiling workflows, and common "gotchas" to address like missing stacks, symbols, or profiling certain languages and events.
Understand and optimize Linux I/O
Understand and optimize Linux I/OAndrea Righi This document summarizes an Italian presentation on monitoring and tuning I/O performance on Linux. It discusses key topics like I/O monitoring tools like iostat, iotop and blktrace, tuning techniques like dirty page writeback and filesystem options, and ensuring reliability of data writes through proper synchronization and error handling. The presentation provides an overview of I/O subsystems in Linux and dives into specific tools and parameters for optimizing I/O.
eBPF maps 101
eBPF maps 101SUSE Labs Taipei The document provides an overview of eBPF maps and how they can be used to share data between eBPF programs running in the kernel and userspace applications. It describes how maps are created via the BPF syscall using the BPF_MAP_CREATE command. It also explains how keys and values can be looked up, updated, and deleted from maps using commands like BPF_MAP_LOOKUP_ELEM, BPF_MAP_UPDATE_ELEM, and BPF_MAP_DELETE_ELEM. Finally, it lists the different types of eBPF maps available.
Tuning Android for low RAM
Tuning Android for low RAMChris Simmonds Slides from Android Builder's Summit 2014 in San Jose, CA
The 4.4 KitKat release includes the results of “Project Svelte”: a set of tweaks to the operating system to make it run more easily on devices with around 512 MiB RAM. This is likely to be especially important for people working with “Embedded Android”, that is, implementing Android on devices that are not smart phones or tablets.
Linux 4.x Tracing Tools: Using BPF Superpowers
Linux 4.x Tracing Tools: Using BPF SuperpowersBrendan Gregg Talk for USENIX LISA 2016 by Brendan Gregg.
"Linux 4.x Tracing Tools: Using BPF Superpowers
The Linux 4.x series heralds a new era of Linux performance analysis, with the long-awaited integration of a programmable tracer: Enhanced BPF (eBPF). Formally the Berkeley Packet Filter, BPF has been enhanced in Linux to provide system tracing capabilities, and integrates with dynamic tracing (kprobes and uprobes) and static tracing (tracepoints and USDT). This has allowed dozens of new observability tools to be developed so far: for example, measuring latency distributions for file system I/O and run queue latency, printing details of storage device I/O and TCP retransmits, investigating blocked stack traces and memory leaks, and a whole lot more. These lead to performance wins large and small, especially when instrumenting areas that previously had zero visibility. Tracing superpowers have finally arrived.
In this talk I'll show you how to use BPF in the Linux 4.x series, and I'll summarize the different tools and front ends available, with a focus on iovisor bcc. bcc is an open source project to provide a Python front end for BPF, and comes with dozens of new observability tools (many of which I developed). These tools include new BPF versions of old classics, and many new tools, including: execsnoop, opensnoop, funccount, trace, biosnoop, bitesize, ext4slower, ext4dist, tcpconnect, tcpretrans, runqlat, offcputime, offwaketime, and many more. I'll also summarize use cases and some long-standing issues that can now be solved, and how we are using these capabilities at Netflix."
Understanding of linux kernel memory model
Understanding of linux kernel memory modelSeongJae Park SeongJae Park introduces himself and his work contributing to the Linux kernel memory model documentation. He developed a guaranteed contiguous memory allocator and maintains the Korean translation of the kernel's memory barrier documentation. The document discusses how the increasing prevalence of multi-core processors requires careful programming to ensure correct parallel execution given relaxed memory ordering. It notes that compilers and CPUs optimize for instruction throughput over programmer goals, and memory accesses can be reordered in ways that affect correctness on multi-processors. Understanding the memory model is important for writing high-performance parallel code.
BPF: Tracing and more
BPF: Tracing and moreBrendan Gregg Video: https://www.youtube.com/watch?v=JRFNIKUROPE . Talk for linux.conf.au 2017 (LCA2017) by Brendan Gregg, about Linux enhanced BPF (eBPF). Abstract:
A world of new capabilities is emerging for the Linux 4.x series, thanks to enhancements that have been included in Linux for to Berkeley Packet Filter (BPF): an in-kernel virtual machine that can execute user space-defined programs. It is finding uses for security auditing and enforcement, enhancing networking (including eXpress Data Path), and performance observability and troubleshooting. Many new open source tools that have been written in the past 12 months for performance analysis that use BPF. Tracing superpowers have finally arrived for Linux!
For its use with tracing, BPF provides the programmable capabilities to the existing tracing frameworks: kprobes, uprobes, and tracepoints. In particular, BPF allows timestamps to be recorded and compared from custom events, allowing latency to be studied in many new places: kernel and application internals. It also allows data to be efficiently summarized in-kernel, including as histograms. This has allowed dozens of new observability tools to be developed so far, including measuring latency distributions for file system I/O and run queue latency, printing details of storage device I/O and TCP retransmits, investigating blocked stack traces and memory leaks, and a whole lot more.
This talk will summarize BPF capabilities and use cases so far, and then focus on its use to enhance Linux tracing, especially with the open source bcc collection. bcc includes BPF versions of old classics, and many new tools, including execsnoop, opensnoop, funcccount, ext4slower, and more (many of which I developed). Perhaps you'd like to develop new tools, or use the existing tools to find performance wins large and small, especially when instrumenting areas that previously had zero visibility. I'll also summarize how we intend to use these new capabilities to enhance systems analysis at Netflix.
Embedded Linux Kernel - Build your custom kernel
Embedded Linux Kernel - Build your custom kernelEmertxe Information Technologies Pvt Ltd Build your own Embedded Linux Kernel by understanding it source code organization, compilation ecosystem and compiling it for a custom target.
New Ways to Find Latency in Linux Using Tracing
New Ways to Find Latency in Linux Using TracingScyllaDB Ftrace is the official tracer of the Linux kernel. It originated from the real-time patch (now known as PREEMPT_RT), as developing an operating system for real-time use requires deep insight and transparency of the happenings of the kernel. Not only was tracing useful for debugging, but it was critical for finding areas in the kernel that was causing unbounded latency. It's no wonder why the ftrace infrastructure has a lot of tooling for seeking out latency. Ftrace was introduced into mainline Linux in 2008, and several talks have been done on how to utilize its tracing features. But a lot has happened in the past few years that makes the tooling for finding latency much simpler. Other talks at P99 will discuss the new ftrace tracers "osnoise" and "timerlat", but this talk will focus more on the new flexible and dynamic aspects of ftrace that facilitates finding latency issues which are more specific to your needs. Some of this work may still be in a proof of concept stage, but this talk will give you the advantage of knowing what tools will be available to you in the coming year.
USENIX ATC 2017: Visualizing Performance with Flame Graphs
USENIX ATC 2017: Visualizing Performance with Flame GraphsBrendan Gregg Talk by Brendan Gregg for USENIX ATC 2017.
"Flame graphs are a simple stack trace visualization that helps answer an everyday problem: how is software consuming resources, especially CPUs, and how did this change since the last software version? Flame graphs have been adopted by many languages, products, and companies, including Netflix, and have become a standard tool for performance analysis. They were published in "The Flame Graph" article in the June 2016 issue of Communications of the ACM, by their creator, Brendan Gregg.
This talk describes the background for this work, and the challenges encountered when profiling stack traces and resolving symbols for different languages, including for just-in-time compiler runtimes. Instructions will be included generating mixed-mode flame graphs on Linux, and examples from our use at Netflix with Java. Advanced flame graph types will be described, including differential, off-CPU, chain graphs, memory, and TCP events. Finally, future work and unsolved problems in this area will be discussed."
Meet cute-between-ebpf-and-tracing
Meet cute-between-ebpf-and-tracingViller Hsiao This document discusses how eBPF (extended Berkeley Packet Filter) can be used for kernel tracing. It provides an overview of BPF and eBPF, how eBPF programs are compiled and run in the kernel, the use of BPF maps, and how eBPF enables new possibilities for dynamic kernel instrumentation through techniques like Kprobes and ftrace.
Understanding eBPF in a Hurry!
Understanding eBPF in a Hurry!Ray Jenkins eBPF is an exciting new technology that is poised to transform Linux performance engineering. eBPF enables users to dynamically and programatically trace any kernel or user space code path, safely and efficiently. However, understanding eBPF is not so simple. The goal of this talk is to give audiences a fundamental understanding of eBPF, how it interconnects existing Linux tracing technologies, and provides a powerful aplatform to solve any Linux performance problem.
BPF - in-kernel virtual machine
BPF - in-kernel virtual machineAlexei Starovoitov BPF of Berkeley Packet Filter mechanism was first introduced in linux in 1997 in version 2.1.75. It has seen a number of extensions of the years. Recently in versions 3.15 - 3.19 it received a major overhaul which drastically expanded it's applicability. This talk will cover how the instruction set looks today and why. It's architecture, capabilities, interface, just-in-time compilers. We will also talk about how it's being used in different areas of the kernel like tracing and networking and future plans.
Linux Performance Analysis: New Tools and Old Secrets
Linux Performance Analysis: New Tools and Old SecretsBrendan Gregg Talk for USENIX/LISA2014 by Brendan Gregg, Netflix. At Netflix performance is crucial, and we use many high to low level tools to analyze our stack in different ways. In this talk, I will introduce new system observability tools we are using at Netflix, which I've ported from my DTraceToolkit, and are intended for our Linux 3.2 cloud instances. These show that Linux can do more than you may think, by using creative hacks and workarounds with existing kernel features (ftrace, perf_events). While these are solving issues on current versions of Linux, I'll also briefly summarize the future in this space: eBPF, ktap, SystemTap, sysdig, etc.
UM2019 Extended BPF: A New Type of Software
UM2019 Extended BPF: A New Type of SoftwareBrendan Gregg BPF (Berkeley Packet Filter) has evolved from a limited virtual machine for efficient packet filtering to a new type of software called extended BPF. Extended BPF allows for custom, efficient, and production-safe performance analysis tools and observability programs to be run in the Linux kernel through BPF. It enables new event-based applications running as BPF programs attached to various kernel events like kprobes, uprobes, tracepoints, sockets, and more. Major companies like Facebook, Google, and Netflix are using BPF programs for tasks like intrusion detection, container security, firewalling, and observability with over 150,000 AWS instances running BPF programs. BPF provides a new program model and security features compared
Similar to Introduction to Perf (20)
Always-on Profiling of All Linux Threads, On-CPU and Off-CPU, with eBPF & Con...
Always-on Profiling of All Linux Threads, On-CPU and Off-CPU, with eBPF & Con...ScyllaDB In this session, Tanel introduces a new open source eBPF tool for efficiently sampling both on-CPU events and off-CPU events for every thread (task) in the OS. Linux standard performance tools (like perf) allow you to easily profile on-CPU threads doing work, but if we want to include the off-CPU timing and reasons for the full picture, things get complicated. Combining eBPF task state arrays with periodic sampling for profiling allows us to get both a system-level overview of where threads spend their time, even when blocked and sleeping, and allow us to drill down into individual thread level, to understand why.
Kernel Recipes 2017 - Using Linux perf at Netflix - Brendan Gregg
Kernel Recipes 2017 - Using Linux perf at Netflix - Brendan GreggAnne Nicolas
Linux perf is a crucial performance analysis tool at Netflix, and is used by a self-service GUI for generating CPU flame graphs and other reports. This sounds like an easy task, however, getting perf to work properly in VM guests running Java, Node.js, containers, and other software, has been at times a challenge. This talk summarizes Linux perf, how we use it at Netflix, the various gotchas we have encountered, and a summary of advanced features.
Brendan Gregg, Netflix
linux monitoring and performance tunning 
linux monitoring and performance tunning iman darabi howto monitor linux server? what metrics are important when monitor server? what is related between metrics and monitoring tools? what are basic linux server optimization ? howto optimize ?
Monitorama 2015 Netflix Instance Analysis
Monitorama 2015 Netflix Instance AnalysisBrendan Gregg Monitorama 2015 talk by Brendan Gregg, Netflix. With our large and ever-changing cloud environment, it can be vital to debug instance-level performance quickly. There are many instance monitoring solutions, but few come close to meeting our requirements, so we've been building our own and open sourcing them. In this talk, I will discuss our real-world requirements for instance-level analysis and monitoring: not just the metrics and features we desire, but the methodologies we'd like to apply. I will also cover the new and novel solutions we have been developing ourselves to meet these needs and desires, which include use of advanced Linux performance technologies (eg, ftrace, perf_events), and on-demand self-service analysis (Vector).
Capturing comprehensive storage workload traces in windows
Capturing comprehensive storage workload traces in windowsBruce Worthington - The document discusses tools for capturing comprehensive storage workload traces in Windows, including Event Tracing for Windows (ETW), xperf, and xperfinfo.
- Examples of traces that will be provided include benchmarks like TPC-C and production workloads from SQL Server, Exchange, and file servers.
- The goal is to supply researchers with long-term real-world traces and tools to enable better storage performance analysis and research.
Opmanager Workshop - Middle East
Opmanager Workshop - Middle EastManageEngine, Zoho Corporation The document summarizes the key features and capabilities of OpManager, a network monitoring software. It discusses how OpManager can be used to discover devices, monitor performance and availability, set alarms and notifications, create reports, and automate management tasks through its rule engine and workflows. The presentation provides examples of monitoring servers, applications, virtual infrastructure, networks, bandwidth and other IT components.
Dpdk 2019-ipsec-eventdev
Dpdk 2019-ipsec-eventdevHemant Agrawal 1) The document discusses adding event device support to the ipsec-gw application in DPDK to improve efficiency.
2) It describes how cryptodev works with poll and event modes and introduces an event crypto adapter to connect crypto queues to the event framework.
3) The proposed changes are to assign all crypto and ethernet queues to an event device and dequeue packets from the event device rather than individual devices to allow asynchronous and CPU-efficient processing.
2010 02 instrumentation_and_runtime_measurement
2010 02 instrumentation_and_runtime_measurementPTIHPA VampirTrace provides instrumentation and run-time measurement capabilities. It allows for automatic, manual, and binary instrumentation. Run-time measurement includes collecting trace data behind the scenes and post-processing. Users have options to configure various settings like environment variables, hardware performance counters, memory allocation counters, filtering, and grouping. FAQ and troubleshooting information is also available.
Linux Performance Tunning Kernel
Linux Performance Tunning KernelShay Cohen This document discusses the Linux kernel architecture. It describes the Linux kernel as a monolithic kernel that supports system calls, loadable modules, preemptive multitasking, virtual memory, and other features. It outlines the kernel's layers and function libraries. It also discusses how the kernel handles processes and context switching, and how it uses a CPU scheduler to allocate processing time between tasks. Finally, it covers inter-process communication techniques like message passing, shared memory, and pipes that allow processes to exchange data.
Cache profiling on ARM Linux
Cache profiling on ARM LinuxPrabindh Sundareson Peemuperf is a Linux kernel module and userspace tool that uses the Performance Monitoring Unit (PMU) on ARM processors to monitor performance metrics like CPU cycles, cache misses, and stalls. It can profile the ARM Cortex A8 and A9 by dynamically configuring the number and types of performance counters. The tool outputs profiling data to the Linux proc filesystem for inspection in userspace. Peemuperf aims to provide cache monitoring capabilities for ARM devices where the oprofile tool is currently limited.
Linux monitoring and Troubleshooting for DBA's
Linux monitoring and Troubleshooting for DBA'sMydbops This presentation by MyDBOPS team shows the various monitoring commands and tools in Linux for the effective operation of DBA's.
ch2.pptx
ch2.pptxHalogens This document provides information about the features and architecture of the Intel Pentium processor. It discusses the processor's superscalar design with dual integer pipelines, branch prediction using a branch target buffer, and separate instruction and data caches. It also describes the floating point unit's pipelined design. The document compares the Pentium to the Intel 80386, noting the Pentium's higher performance enabled by its superscalar design, on-chip caches, branch prediction, and 64-bit data bus. It briefly outlines additional performance features of the Pentium Pro such as its 14-stage pipeline, integrated L2 cache, and out-of-order execution capabilities.
Linux Internals - Part II
Linux Internals - Part IIEmertxe Information Technologies Pvt Ltd The second part of Linux Internals covers system calls, process subsystem and inter process communication mechanisms. Understanding these services provided by Linux are essential for embedded systems engineer.
Unix kernal
Unix kernalShehrevar Davierwala The document discusses the key components and functions of the Unix system kernel. It describes the kernel as managing system resources like CPUs, memory and I/O devices. The major components are the process control subsystem, file subsystem, and hardware control. The kernel handles process management, device management, file management and provides services like virtual memory and networking. It uses a scheduler to allocate CPU time to processes based on their state and priority level.
Perf stat windows
Perf stat windowsAccenture Perfstat is a tool for capturing performance and configuration statistics from Windows hosts and NetApp storage controllers. It uses rsh/ssh to remotely collect data. Perfstat can capture data from one Windows system and multiple controllers simultaneously over a specified time period. It is commonly used to monitor systems for 24 hours by running multiple short samples throughout the day rather than one long sample.
AS & A Level Computer Science Chapter 4 Presentation
AS & A Level Computer Science Chapter 4 PresentationArnelAvila6 Chapter 4 Presentation
IGCSE Computer Science AS & A Level
AOS Lab 6: Scheduling
AOS Lab 6: SchedulingZubair Nabi The document discusses process scheduling in an operating system. It describes how an OS runs more processes than it has processors by providing each process with a virtual processor and multiplexing these across physical processors. When a process performs I/O or its time quantum expires, the scheduler selects another process to run using a timer interrupt. Context switching involves saving the context of the current process and restoring the next process using the swtch function. The scheduler runs in a loop, acquiring the process table lock to select a RUNNABLE process and releasing it to allow other CPUs access between iterations.
Linux Internals - Kernel/Core
Linux Internals - Kernel/CoreShay Cohen The document provides an overview of the Linux kernel architecture and processes. It discusses key kernel concepts like the monolithic kernel design, system calls, loadable modules, virtual memory, and preemptive multitasking. It also covers kernel functions, layers, and context switching between processes. The CPU scheduler, multi-threading, inter-process communication techniques, and tunable kernel parameters are summarized as well.
Ad
More from Wang Hsiangkai (13)
Debug Line Issues After Relaxation.
Debug Line Issues After Relaxation.Wang Hsiangkai The document discusses issues with line number information in the .debug_line section when relocation is not applied properly. It shows a C program with line number information extracted using llvm-dwarfdump and llvm-objdump. Setting a breakpoint at line 6 fails as the address is incorrect without relocation applied to .debug_line. Precise line number information relies on correct address information in .debug_line.
Machine Trace Metrics
Machine Trace MetricsWang Hsiangkai 1. The document describes methods for computing trace metrics like instruction depth, height, and critical path length for basic blocks. It involves analyzing data dependencies between instructions within and across blocks to determine earliest issue cycles while traversing the trace in postorder and inverse postorder.
2. Key steps include finding the best predecessor and successor blocks for each block, computing depth and height values bottom-up and top-down based on dependency latencies, and tracking register liveness across blocks to determine the overall critical path.
Instruction Combine in LLVM
Instruction Combine in LLVMWang Hsiangkai Introduction to instruction combine in LLVM, including instcombine optimization pass, DAGCombine, and MachineCombiner.
GCC LTO
GCC LTOWang Hsiangkai The document describes the GCC Link Time Optimization (LTO) compilation process. It shows how GCC and the linker communicate via a transfer vector in the liblto_plugin.so library. GCC generates LTO objects and passes symbol information to the linker. The lto-wrapper driver then performs the LTO and code generation steps, outputting new object files for the linker to incorporate into the final executable.
LTO plugin
LTO pluginWang Hsiangkai This document describes the LTO plugin interface between GCC and the linker. It explains that the GCC plugin collects options and registers callbacks with the linker via a transfer vector. The linker then calls these callbacks during the link process to handle claim files, get symbol resolutions, and clean up files. The plugin adds symbols, gets symbol information, and adds input files to integrate LTO optimization into the linking stage.
Something About Dynamic Linking
Something About Dynamic LinkingWang Hsiangkai The document discusses the ELF file format and dynamic linking process. It describes the ELF header, program header table, and segments that make up an ELF file. The dynamic linker loads segments into memory, resolves symbols using hash tables, and initializes shared libraries and the main executable in the correct order. Symbol resolution involves determining the symbol hash, searching hash buckets in each library, and comparing names.
DWARF Data Representation
DWARF Data RepresentationWang Hsiangkai The document describes the DWARF debugging format specification. It includes sections for debug_info, debug_abbrev, debug_line, debug_frame, and debug_aranges. The debug_info section contains compilation unit headers and debugging information entries. The debug_abbrev section contains abbreviation declarations that specify tags and attributes for debugging information entries. The debug_frame section contains Call Frame Information including CIE and FDE entries that describe how registers are saved and restored by procedures.
Effective Modern C++
Effective Modern C++Wang Hsiangkai The document discusses various aspects of template type deduction in C++ including:
1. How the type T and the type of the parameter ParamType are deduced based on whether ParamType is a pointer/reference, universal reference, or neither.
2. How array arguments are handled differently when passed by value versus by reference in templates.
3. How function arguments are treated, with function types decaying to function pointers.
4. The differences between auto type deduction and template type deduction.
5. How decltype can be used to deduce types from expressions while preserving reference-ness.
GCC GENERIC
GCC GENERICWang Hsiangkai GENERIC provides a language-independent way to represent program structures and expressions as tree structures. The document outlines the different types of tree nodes used to represent various language elements like declarations, statements, functions, and types. Key nodes include IDENTIFIER_NODE for identifiers, TYPE for type specifications, DECLARATIONS for declarations, STATEMENTS for statements, and FUNCTION_DECL for function definitions. Attributes are represented as TREE_LIST structures and containers can be either TREE_LIST or TREE_VEC nodes.
LLVM Register Allocation (2nd Version)
LLVM Register Allocation (2nd Version)Wang Hsiangkai The document discusses register allocation techniques in LLVM. It introduces the register allocation problem and describes LLVM's template method for register allocation. It then discusses LLVM's basic greedy register allocation approach, which prioritizes assigning registers to live intervals with higher spill costs. Finally, it describes LLVM's greedy register allocation in more detail, including techniques for live interval splitting such as local, instruction, region and block splitting using algorithms like Hopfield networks.
Perf File Format
Perf File FormatWang Hsiangkai The document describes the perf file format specification. It details the structure of a perf.data file which contains a perf_file_header, lists of event ids and attributes, and sections for optional feature data. The perf_file_header includes offsets and sizes to locate this event and feature data. Event attributes describe monitored performance counters, and feature sections contain optional profiling data like CPU topology.
LLVM Register Allocation
LLVM Register AllocationWang Hsiangkai The document discusses register allocation in LLVM. It begins with an introduction to the register allocation problem and describes LLVM's base register allocation interface. It then provides more details on LLVM's basic register allocation approach and its greedy register allocation approach. The greedy approach uses techniques like live range splitting to improve register allocation.
SSA - PHI-functions Placements
SSA - PHI-functions PlacementsWang Hsiangkai The document discusses the process of converting a program into static single assignment (SSA) form. It involves inserting phi-functions at join points to rename variables and ensure each variable has a single definition. Phi-functions are inserted at nodes in the dominance frontier of a definition, which contains predecessors that are not strictly dominated. The dominance frontier and dominator tree properties are used to determine where to place phi-functions to construct the SSA form.
Ad
Recently uploaded (20)
Landscape of Requirements Engineering for/by AI through Literature Review
Landscape of Requirements Engineering for/by AI through Literature ReviewHironori Washizaki Hironori Washizaki, "Landscape of Requirements Engineering for/by AI through Literature Review," RAISE 2025: Workshop on Requirements engineering for AI-powered SoftwarE, 2025.
How to Batch Export Lotus Notes NSF Emails to Outlook PST Easily?
How to Batch Export Lotus Notes NSF Emails to Outlook PST Easily?steaveroggers Migrating from Lotus Notes to Outlook can be a complex and time-consuming task, especially when dealing with large volumes of NSF emails. This presentation provides a complete guide on how to batch export Lotus Notes NSF emails to Outlook PST format quickly and securely. It highlights the challenges of manual methods, the benefits of using an automated tool, and introduces eSoftTools NSF to PST Converter Software — a reliable solution designed to handle bulk email migrations efficiently. Learn about the software’s key features, step-by-step export process, system requirements, and how it ensures 100% data accuracy and folder structure preservation during migration. Make your email transition smoother, safer, and faster with the right approach.
Read More:- https://www.esofttools.com/nsf-to-pst-converter.html
Maxon CINEMA 4D 2025 Crack FREE Download LINK
Maxon CINEMA 4D 2025 Crack FREE Download LINKyounisnoman75 ⭕️➡️ FOR DOWNLOAD LINK : http://drfiles.net/ ⬅️⭕️
Maxon Cinema 4D 2025 is the latest version of the Maxon's 3D software, released in September 2024, and it builds upon previous versions with new tools for procedural modeling and animation, as well as enhancements to particle, Pyro, and rigid body simulations. CG Channel also mentions that Cinema 4D 2025.2, released in April 2025, focuses on spline tools and unified simulation enhancements.
Key improvements and features of Cinema 4D 2025 include:
Procedural Modeling: New tools and workflows for creating models procedurally, including fabric weave and constellation generators.
Procedural Animation: Field Driver tag for procedural animation.
Simulation Enhancements: Improved particle, Pyro, and rigid body simulations.
Spline Tools: Enhanced spline tools for motion graphics and animation, including spline modifiers from Rocket Lasso now included for all subscribers.
Unified Simulation & Particles: Refined physics-based effects and improved particle systems.
Boolean System: Modernized boolean system for precise 3D modeling.
Particle Node Modifier: New particle node modifier for creating particle scenes.
Learning Panel: Intuitive learning panel for new users.
Redshift Integration: Maxon now includes access to the full power of Redshift rendering for all new subscriptions.
In essence, Cinema 4D 2025 is a major update that provides artists with more powerful tools and workflows for creating 3D content, particularly in the fields of motion graphics, VFX, and visualization.
Automation Techniques in RPA - UiPath Certificate
Automation Techniques in RPA - UiPath CertificateVICTOR MAESTRE RAMIREZ Automation Techniques in RPA - UiPath Certificate
Exploring Code Comprehension in Scientific Programming: Preliminary Insight...
Exploring Code Comprehension in Scientific Programming: Preliminary Insight...University of Hawai‘i at Mānoa This presentation explores code comprehension challenges in scientific programming based on a survey of 57 research scientists. It reveals that 57.9% of scientists have no formal training in writing readable code. Key findings highlight a "documentation paradox" where documentation is both the most common readability practice and the biggest challenge scientists face. The study identifies critical issues with naming conventions and code organization, noting that 100% of scientists agree readable code is essential for reproducible research. The research concludes with four key recommendations: expanding programming education for scientists, conducting targeted research on scientific code quality, developing specialized tools, and establishing clearer documentation guidelines for scientific software.
Presented at: The 33rd International Conference on Program Comprehension (ICPC '25)
Date of Conference: April 2025
Conference Location: Ottawa, Ontario, Canada
Preprint: https://arxiv.org/abs/2501.10037
Avast Premium Security Crack FREE Latest Version 2025
Avast Premium Security Crack FREE Latest Version 2025mu394968 🌍📱👉COPY LINK & PASTE ON GOOGLE https://dr-kain-geera.info/👈🌍
Avast Premium Security is a paid subscription service that provides comprehensive online security and privacy protection for multiple devices. It includes features like antivirus, firewall, ransomware protection, and website scanning, all designed to safeguard against a wide range of online threats, according to Avast.
Key features of Avast Premium Security:
Antivirus: Protects against viruses, malware, and other malicious software, according to Avast.
Firewall: Controls network traffic and blocks unauthorized access to your devices, as noted by All About Cookies.
Ransomware protection: Helps prevent ransomware attacks, which can encrypt your files and hold them hostage.
Website scanning: Checks websites for malicious content before you visit them, according to Avast.
Email Guardian: Scans your emails for suspicious attachments and phishing attempts.
Multi-device protection: Covers up to 10 devices, including Windows, Mac, Android, and iOS, as stated by 2GO Software.
Privacy features: Helps protect your personal data and online privacy.
In essence, Avast Premium Security provides a robust suite of tools to keep your devices and online activity safe and secure, according to Avast.
Designing AI-Powered APIs on Azure: Best Practices& Considerations
Designing AI-Powered APIs on Azure: Best Practices& ConsiderationsDinusha Kumarasiri AI is transforming APIs, enabling smarter automation, enhanced decision-making, and seamless integrations. This presentation explores key design principles for AI-infused APIs on Azure, covering performance optimization, security best practices, scalability strategies, and responsible AI governance. Learn how to leverage Azure API Management, machine learning models, and cloud-native architectures to build robust, efficient, and intelligent API solutions
How Valletta helped healthcare SaaS to transform QA and compliance to grow wi...
How Valletta helped healthcare SaaS to transform QA and compliance to grow wi...Egor Kaleynik This case study explores how we partnered with a mid-sized U.S. healthcare SaaS provider to help them scale from a successful pilot phase to supporting over 10,000 users—while meeting strict HIPAA compliance requirements.
Faced with slow, manual testing cycles, frequent regression bugs, and looming audit risks, their growth was at risk. Their existing QA processes couldn’t keep up with the complexity of real-time biometric data handling, and earlier automation attempts had failed due to unreliable tools and fragmented workflows.
We stepped in to deliver a full QA and DevOps transformation. Our team replaced their fragile legacy tests with Testim’s self-healing automation, integrated Postman and OWASP ZAP into Jenkins pipelines for continuous API and security validation, and leveraged AWS Device Farm for real-device, region-specific compliance testing. Custom deployment scripts gave them control over rollouts without relying on heavy CI/CD infrastructure.
The result? Test cycle times were reduced from 3 days to just 8 hours, regression bugs dropped by 40%, and they passed their first HIPAA audit without issue—unlocking faster contract signings and enabling them to expand confidently. More than just a technical upgrade, this project embedded compliance into every phase of development, proving that SaaS providers in regulated industries can scale fast and stay secure.
Expand your AI adoption with AgentExchange
Expand your AI adoption with AgentExchangeFexle Services Pvt. Ltd. AgentExchange is Salesforce’s latest innovation, expanding upon the foundation of AppExchange by offering a centralized marketplace for AI-powered digital labor. Designed for Agentblazers, developers, and Salesforce admins, this platform enables the rapid development and deployment of AI agents across industries.
Email: [email protected]
Phone: +1(630) 349 2411
Website: https://www.fexle.com/blogs/agentexchange-an-ultimate-guide-for-salesforce-consultants-businesses/?utm_source=slideshare&utm_medium=pptNg
Who Watches the Watchmen (SciFiDevCon 2025)
Who Watches the Watchmen (SciFiDevCon 2025)Allon Mureinik Tests, especially unit tests, are the developers’ superheroes. They allow us to mess around with our code and keep us safe.
We often trust them with the safety of our codebase, but how do we know that we should? How do we know that this trust is well-deserved?
Enter mutation testing – by intentionally injecting harmful mutations into our code and seeing if they are caught by the tests, we can evaluate the quality of the safety net they provide. By watching the watchmen, we can make sure our tests really protect us, and we aren’t just green-washing our IDEs to a false sense of security.
Talk from SciFiDevCon 2025
https://www.scifidevcon.com/courses/2025-scifidevcon/contents/680efa43ae4f5
Not So Common Memory Leaks in Java Webinar
Not So Common Memory Leaks in Java WebinarTier1 app This SlideShare presentation is from our May webinar, “Not So Common Memory Leaks & How to Fix Them?”, where we explored lesser-known memory leak patterns in Java applications. Unlike typical leaks, subtle issues such as thread local misuse, inner class references, uncached collections, and misbehaving frameworks often go undetected and gradually degrade performance. This deck provides in-depth insights into identifying these hidden leaks using advanced heap analysis and profiling techniques, along with real-world case studies and practical solutions. Ideal for developers and performance engineers aiming to deepen their understanding of Java memory management and improve application stability.
Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...
Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.
PDF Reader Pro Crack Latest Version FREE Download 2025
PDF Reader Pro Crack Latest Version FREE Download 2025mu394968 🌍📱👉COPY LINK & PASTE ON GOOGLE https://dr-kain-geera.info/👈🌍
PDF Reader Pro is a software application, often referred to as an AI-powered PDF editor and converter, designed for viewing, editing, annotating, and managing PDF files. It supports various PDF functionalities like merging, splitting, converting, and protecting PDFs. Additionally, it can handle tasks such as creating fillable forms, adding digital signatures, and performing optical character recognition (OCR).
How to Optimize Your AWS Environment for Improved Cloud Performance
How to Optimize Your AWS Environment for Improved Cloud PerformanceThousandEyes How to Optimize Your AWS Environment for Improved Cloud Performance
Get & Download Wondershare Filmora Crack Latest [2025]![Get & Download Wondershare Filmora Crack Latest [2025]](https://cdn.slidesharecdn.com/ss_thumbnails/revolutionizingresidentialwi-fi-250422112639-60fb726f-250429170801-59e1b240-thumbnail.jpg?width=560&fit=bounds)
Get & Download Wondershare Filmora Crack Latest [2025]saniaaftab72555 Copy & Past Link 👉👉
https://dr-up-community.info/
Wondershare Filmora is a video editing software and app designed for both beginners and experienced users. It's known for its user-friendly interface, drag-and-drop functionality, and a wide range of tools and features for creating and editing videos. Filmora is available on Windows, macOS, iOS (iPhone/iPad), and Android platforms.
Explaining GitHub Actions Failures with Large Language Models Challenges, In...
Explaining GitHub Actions Failures with Large Language Models Challenges, In...ssuserb14185 GitHub Actions (GA) has become the de facto tool that developers use to automate software workflows, seamlessly building, testing, and deploying code. Yet when GA fails, it disrupts development, causing delays and driving up costs. Diagnosing failures becomes especially challenging because error logs are often long, complex and unstructured. Given these difficulties, this study explores the potential of large language models (LLMs) to generate correct, clear, concise, and actionable contextual descriptions (or summaries) for GA failures, focusing on developers’ perceptions of their feasibility and usefulness. Our results show that over 80% of developers rated LLM explanations positively in terms of correctness for simpler/small logs. Overall, our findings suggest that LLMs can feasibly assist developers in understanding common GA errors, thus, potentially reducing manual analysis. However, we also found that improved reasoning abilities are needed to support more complex CI/CD scenarios. For instance, less experienced developers tend to be more positive on the described context, while seasoned developers prefer concise summaries. Overall, our work offers key insights for researchers enhancing LLM reasoning, particularly in adapting explanations to user expertise.
https://arxiv.org/abs/2501.16495
Top 10 Client Portal Software Solutions for 2025.docx
Top 10 Client Portal Software Solutions for 2025.docxPortli Discover the top 10 client portal software solutions for 2025. Streamline communication, ensure security, and enhance client experience.
Mastering Fluent Bit: Ultimate Guide to Integrating Telemetry Pipelines with ...
Mastering Fluent Bit: Ultimate Guide to Integrating Telemetry Pipelines with ...Eric D. Schabell It's time you stopped letting your telemetry data pressure your budgets and get in the way of solving issues with agility! No more I say! Take back control of your telemetry data as we guide you through the open source project Fluent Bit. Learn how to manage your telemetry data from source to destination using the pipeline phases covering collection, parsing, aggregation, transformation, and forwarding from any source to any destination. Buckle up for a fun ride as you learn by exploring how telemetry pipelines work, how to set up your first pipeline, and exploring several common use cases that Fluent Bit helps solve. All this backed by a self-paced, hands-on workshop that attendees can pursue at home after this session (https://o11y-workshops.gitlab.io/workshop-fluentbit).
Salesforce Data Cloud- Hyperscale data platform, built for Salesforce.
Salesforce Data Cloud- Hyperscale data platform, built for Salesforce.Dele Amefo Salesforce Data Cloud- Hyperscale data platform, built for Salesforce.
Exploring Code Comprehension in Scientific Programming: Preliminary Insight...
Exploring Code Comprehension in Scientific Programming: Preliminary Insight...University of Hawai‘i at Mānoa
Introduction to Perf
- 2. What is Perf? • Perf is a profiler tool for Linux 2.6+ based systems that abstracts away CPU hardware differences in Linux performance measurements and presents a simple command line interface. • Perf is based on the perf_events interface exported by recent versions of the Linux kernel.
- 3. Events • software events - pure kernel counters • context-switches • hardware events - Performance Monitoring Unit (PMU) • measure micro-architectural events such as the number of cycles, instructions retired, L1 cache misses and so on • hardware cache events - events provided by the CPU • tracepoint events - kernel ftrace infrastructure
- 4. perf stat • For any of the supported events, perf can keep a running count during process execution. • Events are designated using their symbolic names followed by optional unit masks and modifiers. • perf stat -e cycles <command> • perf stat -e cycles:u <command> • perf stat -e cycles,instructions,cache-misses <command>
- 5. Modifiers
- 6. Multiplexing and Scaling Events • If there are more events than counters, the kernel uses time multiplexing (switch frequency = HZ, generally 100 or 1000) to give each event a chance to access the monitoring hardware. • Multiplexing only applies to PMU events. • At the end of the run, the tool scales the count based on total time enabled vs time running. • final_count = raw_count * time_enabled/time_running
- 7. • The perf tool can be used to count events on a per- thread, per-process, per-cpu or system-wide basis.
- 8. • per-thread • the counter only monitors the execution of a designated thread. • When the thread is scheduled out, monitoring stops. • By default, perf stat counts in per-thread mode. • Attaching to a running kernel thread • perf stat -e cycles -t <thread-id>
- 9. • per-process • all threads of the process are monitored • Counts and samples are aggregated at the process level. • The perf_events interface allows for automatic inheritance on fork() and pthread_create(). • Attaching to a running process • perf stat -e cycles -p <pid>
- 10. • per-cpu • all threads running on the designated processors are monitored. • perf stat -e cycles:u,instructions:u -a <command> • perf stat -e cycles:u,instructions:u -a -C 0,2-3 <command>
- 11. perf record • collect profiles on per-thread, per-process and per- cpu basis • This generates an output file called perf.data.
- 12. Event-Based Sampling • By default, perf record uses the cycles event as the sampling event. • The perf_events interface allows two modes to express the sampling period: • the number of occurrences of the event (period) • perf record -e retired_instructions:u -c 2000 <command> • the average rate of samples/sec (frequency) • The perf tool defaults to the average rate. It is set to 1000Hz, or 1000 samples/sec. • perf record -e instructions:u -F 250 <command>
- 13. perf report • Samples collected by perf record are saved into a binary file called, by default, perf.data. The perf report command reads this file and generates a concise execution profile.