CC-4007, Large-Scale Machine Learning on Graphs, by Yucheng Low, Joseph Gonzalez and Carlos Guestrin
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Presentation CC-4007, Large-Scale Machine Learning on Graphs, by Yucheng Low, Joseph Gonzalez and Carlos Guestrin at the AMD Developer Summit (APU13) Nov. 11-13, 2013.
![PageRank
IteraFon
R[j]
Iterate
unFl
convergence:
wji
R[i]
“My
rank
is
weighted
average
of
my
friends’
ranks”
X
R[i] = ↵ + (1 ↵)
wji R[j]
(j,i)2E
!
!
α
is
the
random
reset
probability
wji
is
the
prob.
transiFoning
(similarity)
from
j
to
i](https://image.slidesharecdn.com/cc-4007yuchenglow-131122141226-phpapp02/85/CC-4007-Large-Scale-Machine-Learning-on-Graphs-by-Yucheng-Low-Joseph-Gonzalez-and-Carlos-Guestrin-21-320.jpg)
![How
do
we
program
graph
computaFon?
“Think
like
a
Vertex.”
-‐Malewicz
et
al.
[SIGMOD’10]](https://image.slidesharecdn.com/cc-4007yuchenglow-131122141226-phpapp02/85/CC-4007-Large-Scale-Machine-Learning-on-Graphs-by-Yucheng-Low-Joseph-Gonzalez-and-Carlos-Guestrin-27-320.jpg)
![Update
FuncFons
User-‐defined
program:
applied
to
vertex
transforms
data
in
scope
of
vertex
pagerank(i,
scope){
//
Get
Neighborhood
data
(R[i],
wij,
R[j])
!scope;
//
Update
the
vertex
data
Update
funcFon
applied
(asynchronously)
R[i] ← α + (1− α ) ∑ w ji × R[ j];
in
parallel
unFl
convergence
j∈N [i]
//
Reschedule
Neighbors
if
needed
if
R[i]
changes
then
Many
schedulers
available
eschedule_neighbors_of(i);
r to
prioriFze
computaFon
}
Dynamic
computaLon](https://image.slidesharecdn.com/cc-4007yuchenglow-131122141226-phpapp02/85/CC-4007-Large-Scale-Machine-Learning-on-Graphs-by-Yucheng-Low-Joseph-Gonzalez-and-Carlos-Guestrin-28-320.jpg)
![Natural
Graphs
[Image
from
WikiCommons]](https://image.slidesharecdn.com/cc-4007yuchenglow-131122141226-phpapp02/85/CC-4007-Large-Scale-Machine-Learning-on-Graphs-by-Yucheng-Low-Joseph-Gonzalez-and-Carlos-Guestrin-34-320.jpg)
![Problem:
High
Degree
VerLces
è
High
CommunicaLon
for
Distributed
Updates
Data transmitted
Y
across network
O(# cut edges)
Natural
graphs
do
not
have
low-‐cost
balanced
cuts
[Leskovec
et
al.
08,
Lang
04]
Machine
1
Machine
2
Popular
parFFoning
tools
(MeFs,
Chaco,…)
perform
poorly
[Abou-‐Rjeili
et
al.
06]
Extremely
slow
and
require
substan(al
memory](https://image.slidesharecdn.com/cc-4007yuchenglow-131122141226-phpapp02/85/CC-4007-Large-Scale-Machine-Learning-on-Graphs-by-Yucheng-Low-Joseph-Gonzalez-and-Carlos-Guestrin-40-320.jpg)
![Common
Padern
for
Update
Fncs.
R[j]
wji
R[i]
GraphLab_PageRank(i)
//
Compute
sum
over
neighbors
total
=
0
Gather
InformaLon
foreach(
j
in
in_neighbors(i)):
About
Neighborhood
total
=
total
+
R[j]
*
wji
//
Update
the
PageRank
Apply
Update
to
Vertex
R[i]
=
0.1
+
total
//
Trigger
neighbors
to
run
again
if
R[i]
not
converged
then
Sca7er
Signal
to
Neighbors
foreach(
j
in
out_neighbors(i))
Modify
Edge
Data
&
signal
vertex-‐program
on
j](https://image.slidesharecdn.com/cc-4007yuchenglow-131122141226-phpapp02/85/CC-4007-Large-Scale-Machine-Learning-on-Graphs-by-Yucheng-Low-Joseph-Gonzalez-and-Carlos-Guestrin-44-320.jpg)
![Minimizing
CommunicaFon
in
GL2
PowerGraph:
Vertex
Cuts
Y
CommunicaFon
linear
in
#
spanned
machines
GL2
PowerGraph
includes
novel
vertex
cut
algorithms
%
A
vertex-‐cut
m gains
in
p
Provides
order
of
magnitude
inimizes
erformance
#
machines
per
vertex
Percola(on
theory
suggests
Power
Law
graphs
can
be
split
by
removing
only
a
small
set
of
ver(ces
[Albert
et
al.
2000]
è
Small
vertex
cuts
possible!](https://image.slidesharecdn.com/cc-4007yuchenglow-131122141226-phpapp02/85/CC-4007-Large-Scale-Machine-Learning-on-Graphs-by-Yucheng-Low-Joseph-Gonzalez-and-Carlos-Guestrin-47-320.jpg)
![Triangle
CounLng
on
Twicer
Graph
34.8
Billion
Triangles
Hadoop
1636
Machines
[WWW’11]
423
Minutes
64
Machines
15
Seconds
Why?
Wrong
AbstracLon
$
Broadcast
O(degree2)
messages
per
Vertex
S.
Suri
and
S.
Vassilvitskii,
“CounFng
triangles
and
the
curse
of
the
last
reducer,”
WWW’11](https://image.slidesharecdn.com/cc-4007yuchenglow-131122141226-phpapp02/85/CC-4007-Large-Scale-Machine-Learning-on-Graphs-by-Yucheng-Low-Joseph-Gonzalez-and-Carlos-Guestrin-49-320.jpg)
![Triangle
CounFng
on
Twicer
Graph
40M
Users
Total:
34.8
Billion
Triangles
1.2B
Edges
Hadoop
1636
Machines
423
Minutes
59
Minutes
59
Minutes,
1
Mac
Mini!
GraphChi
GraphLab2
64
Machines,
1024
Cores
15
Seconds
Hadoop results from [Suri & Vassilvitskii '11]](https://image.slidesharecdn.com/cc-4007yuchenglow-131122141226-phpapp02/85/CC-4007-Large-Scale-Machine-Learning-on-Graphs-by-Yucheng-Low-Joseph-Gonzalez-and-Carlos-Guestrin-54-320.jpg)
![GraphLab
1
PageRank(i,
scope){
acc
=
0
for
(j
in
InNeighbors)
{
acc
+=
pr[j]
*
edge[j].weight
}
pr[i]
=
0.15
+
0.85
*
acc
}
Explicitly
described
operaLons
Code is intuitive](https://image.slidesharecdn.com/cc-4007yuchenglow-131122141226-phpapp02/85/CC-4007-Large-Scale-Machine-Learning-on-Graphs-by-Yucheng-Low-Joseph-Gonzalez-and-Carlos-Guestrin-57-320.jpg)
![GraphLab
1
GL2
PowerGraph
Implicit
operaLon
PageRank(i,
scope){
acc
=
0
for
(j
in
InNeighbors)
{
acc
+=
pr[j]
*
edge[j].weight
}
pr[i]
=
0.15
+
0.85
*
acc
}
Explicitly
described
operaLons
gather(edge)
{
return
edge.source.value
*
edge.weight
}
merge(acc1,
acc2)
{
return
accum1
+
accum2
}
Implicit
aggregaLon
apply(v,
accum)
{
v.pr
=
0.15
+
0.85
*
acc
}
Code is intuitive
Need to understand engine
to understand code](https://image.slidesharecdn.com/cc-4007yuchenglow-131122141226-phpapp02/85/CC-4007-Large-Scale-Machine-Learning-on-Graphs-by-Yucheng-Low-Joseph-Gonzalez-and-Carlos-Guestrin-58-320.jpg)
![Fine-‐Grained
PrimiFves
Expose
Neighborhood
OperaLons
through
Parallel
Iterators
R[i] = 0.15 + 0.85
X
(j,i)2E
Y
w[j, i] ⇤ R[j]
PageRankUpdateFunction(Y)
{
Y.pagerank
=
0.15
+
0.85
*
MapReduceNeighbors(
lambda
nbr:
nbr.pagerank*nbr.weight,
lambda
(a,b):
a
+
b
neighbors)
(aggregate sum over
)
}](https://image.slidesharecdn.com/cc-4007yuchenglow-131122141226-phpapp02/85/CC-4007-Large-Scale-Machine-Learning-on-Graphs-by-Yucheng-Low-Joseph-Gonzalez-and-Carlos-Guestrin-62-320.jpg)
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CC-4007, Large-Scale Machine Learning on Graphs, by Yucheng Low, Joseph Gonzalez and Carlos Guestrin
- 1. 7. After 8. After Large-‐Scale Machine Learning and Graphs Yucheng Low
- 2. Phase 1: POSSIBILITY Benz Patent Motorwagen (1886)
- 3. Phase 2: SCALABILITY Model T Ford (1908)
- 5. Possibility 6. Before 7. After 6. Before Scalability + Graph 8. After 7. After 6. Before 8. After 7. After Usability 8. After
- 6. The Big QuesFon of Big Learning How will we design and implement parallel learning systems?
- 7. MapReduce for Data-‐Parallel ML Excellent for large data-‐parallel tasks! Data-Parallel MapReduce Feature ExtracFon Cross ValidaFon CompuFng Sufficient StaFsFcs Graph-Parallel Is there more to Machine Learning Graphical Models Gibbs Sampling Belief PropagaFon VariaFonal Opt. CollaboraLve Filtering Semi-‐Supervised Learning ? Tensor FactorizaFon Label PropagaFon CoEM Graph Analysis PageRank Triangle CounFng
- 8. Es(mate Poli(cal Bias ? ? ? Liberal ? ? Post ? Post ? ? ? Semi-‐Supervised & ? TransducFve Learning Post Post ? ? ? Post Post Post ? ConservaFve ? ? ? ? Post ? Post ? ? ? ? Post ? Post ? Post Post Post ? Post Post ? ? ? ?
- 9. Flashback to 1998 First Google advantage: a Graph Algorithm & a System to Support it!
- 10. The Power of Dependencies where the value is!
- 11. It’s all about the graphs…
- 12. Social Media ! AdverLsing Web Graphs encode the relaLonships between: People ! Science Products Ideas Facts Interests Big: 100 billions of verLces and edges and rich metadata ! ! Facebook (10/2012): 1B users, 144B friendships Twicer (2011): 15B follower edges
- 13. Examples of Graphs in Machine Learning
- 14. CollaboraFve Filtering: ExploiFng Dependencies Women on the Verge of a Nervous Breakdown The CelebraFon Latent Factor Models City of God Matrix CompleFon/FactorizaFon Models Wild Strawberries La Dolce Vita
- 15. Topic Modeling Cat Apple Latent Dirichlet AllocaFon, etc Growth Hat Plant
- 16. Example Topics Discovered from Wikipedia
- 17. Machine Learning Pipeline Data Extract Features Graph Formation Structured Machine Learning Algorithm 6. Before Value from Data 7. After face labels images docs movie raFngs doc topics social acFvity 8. After movie recommend senFment analysis
- 18. ML Tasks Beyond Data-‐Parallelism Data-Parallel Graph-Parallel Map Reduce Feature ExtracFon Cross ValidaFon CompuFng Sufficient StaFsFcs Graphical Models Gibbs Sampling Belief PropagaFon VariaFonal Opt. CollaboraLve Filtering Tensor FactorizaFon Semi-‐Supervised Learning Label PropagaFon CoEM Graph Analysis PageRank Triangle CounFng
- 19. Example of a Graph-‐Parallel Algorithm
- 20. PageRank Depends on rank of who follows them… Depends on rank of who follows her What’s the rank of this user? Rank? Loops in graph è Must iterate!
- 21. PageRank IteraFon R[j] Iterate unFl convergence: wji R[i] “My rank is weighted average of my friends’ ranks” X R[i] = ↵ + (1 ↵) wji R[j] (j,i)2E ! ! α is the random reset probability wji is the prob. transiFoning (similarity) from j to i
- 22. ProperFes of Graph Parallel Algorithms Dependency Graph Local Updates IteraFve ComputaFon My Rank Friends Rank
- 23. The Need for a New AbstracFon ! Need: Asynchronous, Dynamic Parallel ComputaFons Data-Parallel Graph-Parallel Map Reduce Feature ExtracFon Cross ValidaFon CompuFng Sufficient StaFsFcs Graphical Models Gibbs Sampling Belief PropagaFon VariaFonal Opt. CollaboraLve Filtering Tensor FactorizaFon Semi-‐Supervised Learning Label PropagaFon CoEM Data-‐Mining PageRank Triangle CounFng
- 24. The GraphLab Goals Know how to solve ML problem on 1 machine Efficient parallel predicFons
- 25. POSSIBILITY
- 26. Data Graph Data associated with verFces and edges Graph: • Social Network Vertex Data: • User profile text • Current interests esFmates Edge Data: • Similarity weights
- 27. How do we program graph computaFon? “Think like a Vertex.” -‐Malewicz et al. [SIGMOD’10]
- 28. Update FuncFons User-‐defined program: applied to vertex transforms data in scope of vertex pagerank(i, scope){ // Get Neighborhood data (R[i], wij, R[j]) !scope; // Update the vertex data Update funcFon applied (asynchronously) R[i] ← α + (1− α ) ∑ w ji × R[ j]; in parallel unFl convergence j∈N [i] // Reschedule Neighbors if needed if R[i] changes then Many schedulers available eschedule_neighbors_of(i); r to prioriFze computaFon } Dynamic computaLon
- 29. The GraphLab Framework Graph Based Data Representa(on Scheduler Update FuncFons User Computa(on Consistency Model
- 30. AlternaFng Least Squares CoEM Lasso SVD Belief PropagaFon LDA Splash Sampler Bayesian Tensor FactorizaFon PageRank SVM Gibbs Sampling Dynamic Block Gibbs Sampling K-‐Means Linear Solvers …Many others… Matrix FactorizaFon
- 31. Never Ending Learner Project (CoEM) Hadoop 95 Cores 7.5 hrs Distributed GraphLab 32 EC2 machines 80 secs 0.3% of Hadoop time 2 orders of mag faster "# 2 orders of mag cheaper
- 32. ! ! ML algorithms as vertex programs Asynchronous execuFon and consistency models
- 33. Thus far… GraphLab 1 provided exciFng scaling performance But… We couldn’t scale up to Altavista Webgraph 2002 1.4B verLces, 6.7B edges
- 34. Natural Graphs [Image from WikiCommons]
- 35. Problem: ExisFng distributed graph computaFon systems perform poorly on Natural Graphs
- 36. Achilles Heel: Idealized Graph AssumpFon Assumed… Small degree " Easy to parFFon But, Natural Graphs… Many high degree verFces (power-‐law degree distribuFon) " Very hard to parFFon
- 37. Power-‐Law Degree DistribuFon 10 Number of VerFces count 10 8 10 High-‐Degree VerFces: 1% verFces adjacent to 50% of edges 6 10 4 10 2 10 0 10 AltaVista WebGraph 1.4B VerFces, 6.6B Edges 0 10 2 10 4 Degree 10 degree 6 10 8 10
- 38. High Degree VerFces are Common Popular Movies Users “Social” People NeYlix Movies Hyper Parameters θ θ β θ θ Z Z Z Z Z Z Z Z w w Z Z w w Z Z w w Z Z Z w w w Z w w w w w w w Docs α Common Words LDA Obama Words
- 39. Power-‐Law Degree DistribuFon “Star Like” MoFf President Obama Followers
- 40. Problem: High Degree VerLces è High CommunicaLon for Distributed Updates Data transmitted Y across network O(# cut edges) Natural graphs do not have low-‐cost balanced cuts [Leskovec et al. 08, Lang 04] Machine 1 Machine 2 Popular parFFoning tools (MeFs, Chaco,…) perform poorly [Abou-‐Rjeili et al. 06] Extremely slow and require substan(al memory
- 41. acement cutsParFFoning edges: most of the Random ! Both GraphLab 1, Pregel, Twicer, Facebook,… rely on Random (hashed) parFFoning for Natural Graphs m 5.1. If vertices are randomly assign s then the expected fraction of edges cu For p Machines: |Edges Cut| 1 E =1 |E| p Machine 10 Machines Machine e if just twoà 90% of 1 dges cut used, machines are 2 100 Machines à 99% of edges cut! ample ha will be cut requiring order |E| /2 commu All data is communicated… Licle advantage over MapReduce
- 42. In Summary GraphLab 1 and Pregel are not well suited for natural graphs ! ! Poor performance on high-‐degree verFces Low Quality ParFFoning
- 44. Common Padern for Update Fncs. R[j] wji R[i] GraphLab_PageRank(i) // Compute sum over neighbors total = 0 Gather InformaLon foreach( j in in_neighbors(i)): About Neighborhood total = total + R[j] * wji // Update the PageRank Apply Update to Vertex R[i] = 0.1 + total // Trigger neighbors to run again if R[i] not converged then Sca7er Signal to Neighbors foreach( j in out_neighbors(i)) Modify Edge Data & signal vertex-‐program on j
- 45. GAS DecomposiFon Gather (Reduce) Accumulate informaFon about neighborhood Y Y Y ⌃ + + … + $ Scacer Apply the accumulated value to center vertex Σ Y Parallel “Sum” Apply Y Update adjacent edges and verFces. Y’ Y’
- 46. Many ML Algorithms fit into GAS Model graph analyFcs, inference in graphical models, matrix factorizaFon, collaboraFve filtering, clustering, LDA, …
- 47. Minimizing CommunicaFon in GL2 PowerGraph: Vertex Cuts Y CommunicaFon linear in # spanned machines GL2 PowerGraph includes novel vertex cut algorithms % A vertex-‐cut m gains in p Provides order of magnitude inimizes erformance # machines per vertex Percola(on theory suggests Power Law graphs can be split by removing only a small set of ver(ces [Albert et al. 2000] è Small vertex cuts possible!
- 48. 7. After From the AbstracFon to a System 8. After
- 49. Triangle CounLng on Twicer Graph 34.8 Billion Triangles Hadoop 1636 Machines [WWW’11] 423 Minutes 64 Machines 15 Seconds Why? Wrong AbstracLon $ Broadcast O(degree2) messages per Vertex S. Suri and S. Vassilvitskii, “CounFng triangles and the curse of the last reducer,” WWW’11
- 50. Topic Modeling (LDA) Million Tokens Per Second 0 20 60 80 100 120 140 Specifically engineered for this task Smola et al. GL2 PowerGraph 40 64 cc2.8xlarge EC2 Nodes 200 lines of code & 4 human hours ! English language Wikipedia ! ! 2.6M Documents, 8.3M Words, 500M Tokens ComputaFonally intensive algorithm 100 Yahoo! Machines 160
- 51. How well does GraphLab scale? Yahoo Altavista Web Graph (2002): One of the largest publicly available webgraphs 1.4B Webpages, 6.7 Billion Links 7 seconds per iter. 64 HPC Nodes 1B links processed per second 30 lines of user code
- 52. GraphChi: Going small with GraphLab 7. After 8. After Solve huge problems on small or embedded devices? Key: Exploit non-‐volaFle memory (starFng with SSDs and HDs)
- 53. GraphChi – disk-‐based GraphLab Challenge: Random Accesses Novel GraphChi soluLon: Parallel sliding windows method è minimizes number of random accesses
- 54. Triangle CounFng on Twicer Graph 40M Users Total: 34.8 Billion Triangles 1.2B Edges Hadoop 1636 Machines 423 Minutes 59 Minutes 59 Minutes, 1 Mac Mini! GraphChi GraphLab2 64 Machines, 1024 Cores 15 Seconds Hadoop results from [Suri & Vassilvitskii '11]
- 55. 6. Before ! ! ML algorithms as vertex programs Asynchronous execuFon and consistency models 7. After ! 8. After ! Natural graphs change the nature of computaFon Vertex cuts and gather/apply/scacer model
- 56. GL2 PowerGraph focused on Scalability at the loss of Usability
- 57. GraphLab 1 PageRank(i, scope){ acc = 0 for (j in InNeighbors) { acc += pr[j] * edge[j].weight } pr[i] = 0.15 + 0.85 * acc } Explicitly described operaLons Code is intuitive
- 58. GraphLab 1 GL2 PowerGraph Implicit operaLon PageRank(i, scope){ acc = 0 for (j in InNeighbors) { acc += pr[j] * edge[j].weight } pr[i] = 0.15 + 0.85 * acc } Explicitly described operaLons gather(edge) { return edge.source.value * edge.weight } merge(acc1, acc2) { return accum1 + accum2 } Implicit aggregaLon apply(v, accum) { v.pr = 0.15 + 0.85 * acc } Code is intuitive Need to understand engine to understand code
- 59. Scalability, but very rigid abstracFon Great flexibility, but hit scalability wall (many contorFons needed to implement SVD++, Restricted Boltzmann Machines) What now?
- 61. GL3 WarpGraph Goals Program Like GraphLab 1 Run Like GraphLab 2 Machine 1 Machine 2
- 62. Fine-‐Grained PrimiFves Expose Neighborhood OperaLons through Parallel Iterators R[i] = 0.15 + 0.85 X (j,i)2E Y w[j, i] ⇤ R[j] PageRankUpdateFunction(Y) { Y.pagerank = 0.15 + 0.85 * MapReduceNeighbors( lambda nbr: nbr.pagerank*nbr.weight, lambda (a,b): a + b neighbors) (aggregate sum over ) }
- 63. Expressive, Extensible Neighborhood API Parallel Transform Adjacent Edges Broadcast Y Y Y Modify adjacent edges Schedule a selected subset of adjacent verFces Y + + … + Y Parallel Sum Y MapReduce over Neighbors
- 64. Can express every GL2 PowerGraph program (more easily) in GL3 WarpGraph But GL3 is more expressive MulFple gathers UpdateFunction(v) { if (v.data == 1) accum = MapReduceNeighs(g,m) else ... } Scacer before gather CondiFonal execuFon
- 65. Graph Coloring Twicer Graph: 41M VerFces 1.4B Edges GL2 PowerGraph 227 seconds GL3 WarpGraph 60 seconds 3.8x Faster WarpGraph outperforms PowerGraph with simpler code 32 Nodes x 16 Cores (EC2 HPC cc2.8x)
- 66. 6. Before ! ! ML algorithms as vertex programs Asynchronous execuFon and consistency models 7. After 6. Before ! 8. After ! Natural graphs change the nature of computaFon Vertex cuts and gather/apply/scacer model 7. After 8. After ! ! Usability is key Access neighborhood through parallelizable iterators and latency hiding
- 67. Usability for Whom??? PowerGraph WarpGraph …
- 68. Machine Learning PHASE 3 (part 2) USABILITY
- 69. ExciFng Time to Work in ML With Big Data, I’ll take over the world!!! We met because of Big Data Why won’t Big Data read my mind??? Unique opportuniFes to change the world!! ☺ But, every deployed system is an one-‐off soluFon, and requires PhDs to make work… '
- 70. But… Even basics of scalable ML can be challenging ML key to any new service we want to build 6 months from R/Matlab to producFon, at best State-‐of-‐art ML algorithms trapped in research papers Goal of GraphLab 3: Make huge-‐scale machine learning accessible to all!
- 71. Step 0 : Learn ML With GraphLab Notebook
- 72. GraphLab + Python Step 1 : pip install graphlab prototype on local machine
- 73. GraphLab + Python Step 2 : scale to full dataset in the cloud with minimal code changes
- 74. GraphLab + Python Step 3: deploy in production
- 75. GraphLab + Python Step 4: ???
- 76. GraphLab + Python Step 4: Profit
- 77. GraphLab Toolkits Highly scalable, state-‐of-‐the-‐art machine learning methods… all accessible from python Graph AnalyFcs Graphical Models Computer Vision Clustering Topic Modeling CollaboraFve Filtering
- 78. Now with GraphLab: Learn/Prototype/Deploy Even basics of scalable ML can be challenging 6 months from R/Matlab to producFon, at best State-‐of-‐art ML algorithms trapped in research papers Learn ML with GraphLab Notebook pip install graphlab then deploy on EC2/Cluster Fully integrated via GraphLab Toolkits
- 79. We’re selecFng strategic partners Help define our strategy & prioriFes And, get the value of GraphLab in your company [email protected]
- 80. 8. After C++ GraphLab 2.2 available now: graphlab.com Beta Program: beta.graphlab.com Follow us on Twicer: @graphlabteam Define our future: [email protected] Needless to say: [email protected]