MALT: Distributed Data-Parallelism for Existing ML Applications (Distributed Machine Learning)

MALT: Distributed Data Parallelism for
Existing ML Applications
Hao Li*, Asim Kadav, Erik Kruus, Cristian Ungureanu
* University of Maryland-College Park
NEC Laboratories, Princeton

Software
User-generated
content
Hardware
Data
generated by
Transactions,
website visits,
other metadata
facebook,
twitter, reviews,
emails
Camera feeds,
Sensors
Applications
(usually
based on ML)
Ad-click/Fraud
prediction,
Recommendatio
ns
Sentiment
analysis,
Targeted
advertising
Surveillance,
Anomaly
detection
Data, data everywhere…
2

Timely insights depend on updated models
Surveillance/Safe Driving
• Usually trained in real time
• Expensive to train HD
videos
Advertising (ad prediction,
ad-bidding)
• Usually trained hourly
• Expensive to train
millions of requests
Knowledge banks
(automated answering)
• Usually trained daily
• Expensive to train
large corpus
Data (such as
image, label pairs)
model parameters
Training
Test/Deploy
model parameters
labrado
r
3

Model training challenges
• Large amounts of data to train
• Explosion in types, speed and scale of data
• Types : Image, time-series, structured, sparse
• Speed : Sensor, Feeds, Financial
• Scale : Amount of data generated growing exponentially
• Public datasets: Processed splice genomic dataset is 250 GB and
data subsampling is unhelpful
• Private datasets: Google, Baidu perform learning over TBs of data
• Model sizes can be huge
• Models with billions of parameters do not fit in a single machine
• E.g. : Image classification, Genome detection
Model accuracy generally improves by using
larger models with more data
4

Properties of ML training workloads
• Fine-grained and Incremental:
• Small, repeated updates to model vectors
• Asynchronous computation:
• E.g. Model-model communication, back-propagation
• Approximate output:
• Stochastic algorithms, exact/strong consistency maybe an overkill
• Need rich developer environment:
• Require rich set of libraries, tools, graphing abilities
5

MALT: Machine Learning Toolset
• Run existing ML software in data-parallel fashion
• Efficient shared memory over RDMA writes to
communicate model information
• Communication: Asynchronously push (scatter) model information,
gather locally arrived information
• Network graph: Specify which replicas to send updates
• Representation: SPARSE/DENSE hints to store model vectors
• MALT integrates with existing C++ and Lua applications
• Demonstrate fault-tolerance and speedup with SVM, matrix
factorization and neural networks
• Re-uses existing developer tools
6

Outline
Introduction
Background
MALT Design
Evaluation
Conclusion
7

Distributed Machine Learning
• ML algorithms learn incrementally from data
• Start with an initial guess of model parameters
• Compute gradient over a loss fn, and update the model
• Data Parallelism: Train over large data
• Data split over multiple machines
• Model replicas train over different parts of data and
communicate model information periodically
• Model parallelism: Train over large models
• Models split over multiple machines
• A single training iteration spans multiple machines
8

• SGD trains over one (or few)
training example at a time
• Every data example processed is
an iteration
• Update to the model is gradient
• Number of iterations to compute
gradient is batch size
• One pass over the entire data is
an epoch
• Acceptable performance over test
set after multiple epochs is
convergence
Stochastic Gradient Descent (SGD)
Can train wide range of ML methods : k-means, SVM,
matrix factorization, neural-networks etc.
9

Data-parallel SGD: Mini-batching
• Machines train in parallel over a batch and exchange model
information
• Iterate over data examples faster (in parallel)
• May need more passes over data than single SGD (poor convergence)
10

Approaches to data-parallel SGD
• Hadoop/Map-reduce: A variant of bulk-synchronous parallelism
• Synchronous averaging of model updates every epoch (during reduce)
Data
Data
Data
Data
reducemap
model parameter 1
model parameter 2
model parameter 3
merged model parameter
1)Infrequent communication produces low accuracy models
2)Synchronous training hurts performance due to stragglers
11

Parameter server
• Central server to merge updates every few iterations
• Workers send updates asynchronously and receive whole models from the server
• Central server merges incoming models and returns the latest model
• Example: Distbelief (NIPS 2012), Parameter Server (OSDI 2014), Project Adam (OSDI 2014)
Data
Data
Data
workers server
model parameter 1
model parameter 2
model parameter 3
merged model parameter
12

Peer-to-peer approach (MALT)
• Workers send updates to one another asynchronously
• Workers communicate every few iterations
• No separate master/slave code to port applications)
• No central server/manager: simpler fault tolerance
DataData Data Data
workers
model parameter 1 model parameter 2 model parameter 3 model parameter 4
13

Outline
Introduction
Background
MALT Design
Evaluation
Conclusion
14

MALT framework
infiniBand communication substrate (such as MPI, GASPI)
MALT dStorm
(distributed one-sided remote memory)
MALT VOL
(Vector Object Library)
Existing ML
applications
MALT VOL
Existing ML
applications
MALT VOL
Existing ML
applications
Model replicas train in parallel. Use shared memory to
communicate. Distributed file system to load datasets in parallel.
16

O3O2
dStorm: Distributed one-sided remote memory
• RDMA over infiniBand allows high-throughput/low latency networking
• RDMA over Converged Ethernet (RoCE) support for non-RDMA hardware
• Shared memory abstraction based over RDMA one-sided writes (no reads)
Memory Memory Memory
O2 O3
S1.create(size, ALL) S2.create(size, ALL) S3.create(size, ALL)
O1 O3 O1 O2O2O1 O3
Machine 1 Machine 2 Machine 3
Similar to partitioned global address space languages -
Local vs Global memory
17
Primary
copy
Receive
queue for O1
Receive
queue for O1

MemoryMemory
O2 O3O2
scatter() propagates using one-sided RDMA
• Updates propagate based on communication graph
Memory
O1 O2 O3
S1.scatter() S2.scatter() S3.scatter()
O1 O2 O3 O1 O3O2O1 O2O1 O3
Remote CPU not involved: Writes over RDMA. Per-sender
copies do not need to be immediately merged by receiver
18

MemoryMemory
O2 O3O2
gather() function merges locally
• Takes a user-defined function (UDF) as input such as average
Memory
O1 O2 O3
S1.gather(AVG) S2.gather(AVG) S3.gather(AVG)
O1 O2 O3 O1 O3O2O1O1 O3O2 O3O3 O1 O1 O2
Useful/General abstraction for data-parallel algos: Train and
scatter() the model vector, gather() received updates
19

VOL: Vector Object Library
• Expose vectors/tensors instead of
memory objects
• Provide representation optimizations
• sparse/dense parameters store as
arrays or key-value stores
• Inherits scatter()/gather() calls
from dStorm
• Can use vectors/tensors in existing
vectors
Memory
V1
20
O1

Propagating updates to everyone
Data
Data
Data
Data
Data
Data
model 6
model 5
model 1
model 2
model 3
model 4
21

O(N
2
) communication rounds for N nodes
Data
Data
Data
Data
Data
Data
model 6
model 5
model 4
model 3
model 2
model 1
22

In-direct propagation of model updates
Data
Data
Data
Data
Data
Data
model 6
model 5
model 1
model 3
model 4
model 2
23
Use a uniform random sequence to determine where to
send updates to ensure all updates propagate uniformly.
Each node sends to fewer than N nodes (such as logN)

O(Nlog(N)) communication rounds for N nodes
Data
Data
Data
Data
Data
Data
• MALT proposes sending models to
fewer nodes (log N instead of N)
• Requires the node graph be
connected
• Use any uniform random sequence
• Reduces processing/network times
• Network communication time reduces
• Time to update the model reduces
• Iteration speed increases but may
need more epochs to converge
• Key Idea: Balance communication
with computation
• Send to less/more than log(N) nodes
25
Trade-off model
information recency with
savings in network and
update processing time

Converting serial algorithms to parallel
Serial SGD
Gradient g;
Parameter w;
for epoch = 1:maxEpochs do
for i = 1:N do
g = cal_gradient(data[i]);
w = w + g;
Data-Parallel SGD
• scatter() performs one-sided RDMA writes to other machines.
• “ALL” signifies communication with all other machines.
• gather(AVG) applies average to the received gradients.
• Optional barrier() makes the training synchronous.
maltGradient g(sparse, ALL);
Parameter w;
for epoch = 1:maxEpochs do
for i = 1:N/ranks do
g = cal_gradient(data[i]);
g.scatter(ALL);
g.gather(AVG);
w = w + g;
26

Consistency guarantees with MALT
• Problem: Asynchronous scatter/gather may cause models to
diverge significantly
• Problem scenarios:
• Torn reads: Model information may get re-written while being read
• Stragglers: Slow machines send stale model updates
• Missed updates: Sender may overwrite its queue if receiver is slow
• Solution: All incoming model updates carry iteration count in their header
and trailer
• Read header-trailer-header to skip torn reads
• Slow down current process if the incoming updates are too stale to limit
stragglers and missed updates (Bounded Staleness [ATC 2014])
• Few inconsistencies are OK for model training (Hogwild [NIPS 2011])
• Use barrier to train in BSP fashion for stricter guarantees
27

MALT fault tolerance: Remove failed peers
Distributed file-system (such as NFS or
HDFS)
MALT dStorm
(distributed one-sided remote memory)
VOL
Application
VOL
Application
VOL
Application
fault
monitor
• Each replica has a fault monitor
• Detects local failures (processor
exceptions such as divide-by-zero)
• Detects failed remote writes
(timeouts)
• When failure occurs
• Locally: Terminate local training,
other monitors see failed writes
• Remote: Communicate with other
monitors and create a new group
• Survivor nodes: Re-register queues,
re-assign data, and resume training
• Cannot detect byzantine failures
(such as corrupt gradients)
28

Outline
Introduction
Background
MALT Design
Evaluation
Conclusion
29

Integrating existing ML applications with MALT
• Support Vector Machines (SVM)
• Application: Various classification applications
• Existing application: Leon Bottou’s SVM SGD
• Datasets: RCV1, PASCAL suite, splice (700M - 250 GB size, 47K -
16.6M parameters)
• Matrix Factorization (Hogwild)
• Application: Movie recommendation (Netflix)
• Existing application: HogWild (NIPS 2011)
• Datasets: Netflix (1.6 G size, 14.9M parameters)
• Neural networks (NEC RAPID)
• Application: Ad-click prediction (KDD 2012)
• Existing application: NEC RAPID (Lua frontend, C++ backend)
• Datasets: KDD 2012 (3.1 G size, 12.8M parameters)
+ +
++
---- -
-
M U
V
=
30
Cluster: Eight Intel 2.2 Ghz with 64 GB RAM machines
connected with Mellanox 56 Gbps infiniband backplane

Speedup using SVM-SGD with RCV1 dataset
goal: Loss value as achieved by
single rank SGD
cb size : Communication batch
size - Data examples processed
before model communication
31

Speedup using RAPID with KDD 2012 dataset
32
MALT provides speedup over single process SGD

Speedup with the Halton scheme
Indirect propagation of model improves performance
33
Goal: 0.01245
BSP: Bulk-Synchronous Processing
ASYNC: Asynchronous 6X
ASYNC Halton: Asynchronous with the
Halton network 11X

Data transferred over the network for different designs
34
MALT-Halton provides network efficient learning

Conclusions
• MALT integrates with existing ML software to provide data-
parallel learning
• General purpose scatter()/gather() API to send model updates using
one-sided communication
• Mechanisms for network/representation optimizations
• Supports applications written in C++ and Lua
• MALT provides speedup and can process large datasets
• More results on speedup, network efficiency, consistency models, fault
tolerance, and developer efforts in the paper
• MALT uses RDMA support to reduce model propagation costs
• Additional primitives such as fetch_and_add() may further reduce model
processing costs in software
35

Other approaches to parallel SGD
• GPUs
• Orthogonal approach to MALT, MALT segments can be created over
GPUs
• Excellent for matrix operations, smaller sized models
• However, a single GPU memory is limited to 6-10 GB
• Communication costs dominate for larger models
• Small caches: Require regular memory access for good performance
• Techniques like sub-sampling/dropout perform poorly
• Hard to implement convolution (need matrix expansion for good performance)
• MPI/Other global address space approaches
• Offer a low level API (e.g. perform remote memory management)
• A system like MALT can be built over MPI
38

Evaluation setup
Application Model Dataset/Size
# training
items
# testing
items
# parameters
Document
Classification
SVM
RCV1/480M 781K 23K 47K
Image
Classification
Alpha/1G 250K 250K 500
DNA
Classification
DNA/10G 23M 250K 800
Webspam
detection
webspam/10G 250K 100K 16.6M
Genome
classification
splice-
site/250G
10M 111K 11M
Collaborative
filtering
Matrix
factorization
netflix/1.6G 100M 2.8M 14.9M
Click Prediction
Neural
networks
KDD2012/
3.1G
150M 100K 12.8M
• Eight Intel 8-core, 2.2 GHz Ivy-Bridge, with 64 GB
• All machines connected via Mellanox 56 Gbps infiniband
39

Speedup using NMF with netflix dataset
40

Data
Data
Data
Data
Data
Data
model 1
model 2
model 3
model 4
model 6
model 5
Halton sequence: n —> n/2, n/4, 3n/4, 5n/8, 7n/8
41

Speedup with different consistency models
Benefit with asynchronous training
42
• BSP: Bulk-synchronous
parallelism (training with
barriers)
• ASYNC: Fully asynchronous
training
• SSP: Stale synchronous
parallelism (limit stragglers
by stalling fore-runners)

Developer efforts to make code data-parallel
Application Dataset LOC Modified LOC Added
SVM RCV1 105 107
Matrix
Factorization
Netflix 76 82
Neural
Network
KDD 2012 82 130
On an average, about 15% of lines modified/added
44

Fault tolerance
45
MALT provides robust model training

MALT: Distributed Data-Parallelism for Existing ML Applications (Distributed Machine Learning)

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