Using GPUs to Handle Big Data with Java

Tim Ellison – IBM Java Technology Center
October 2nd, 2014
Using GPUs to Handle Big
Data with Java
CON3285
© 2014 IBM Corporation

© 2014 IBM Corporation
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2

About me
 Based in the Java Technology Centre, Hursley UK
 Working on various runtime technologies for >20 years
 Experience of open source communities
 Currently focused on class library design and delivery
 Overall technical lead for IBM Java 8 SE
tim_ellison@uk.ibm.com
@tpellison
© 2014 3 IBM Corporation

Goals of this talk
 Provide an introduction to programming with GPUs
 Show how we can bring a popular GPU programming model to Java
 Demonstrate the effect of GPUs on solving Big Data problems
 Describe seamlessly enabling the Java implementation to use GPUs

Introduction to Graphics Processing Units
 GPUs are no longer solely targeted at single purpose graphical
operations such as rendering and texture mapping
 Modern high-end GPUs are general purpose “stream processors”
 Provide substantially more FLOPs per $ and per watt than CPUs
 Programming language extensions allow a flow of control from
the CPU to the single instruction, multiple thread (SIMT) engine

Harnessing the power of GPUs
Theoretical GFLOP/s
With their massively parallel architecture, GPUs far
exceed the computational power of CPUs when
operating on large sets of floating point numbers.
How can we bring this
capability to Java?
http://docs.nvidia.com/cuda/cudacprogrammingguide

Introduction to Programming GPUs
 Typical scenario for heterogeneous programming:
– Host computer with CPU(s) and GPU(s) installed
on PCIe bus
– Programmer identifies parallelizable, compute
intensive routine, and codes to GPU
– Flow of data and control passes between CPU
host and GPU device under control of host device
Particularly suited to scientific and numerical analysis problems (e.g. linear algebra)
We have focused on Nvidia CUDA as the programming model for exploiting GPUs.

CUDA – a simple example
Vector addition
a b c
++
+
=
==
+
++
===
Serially on the CPU...
void add( int *a, int *b, int *c ) {
for (i=0; i < N; i++) {
c[i] = a[i] + b[i];
}
}
On the CPU ...
void cuda_add(int *a, int *b, int *c) {
int *dev_a, *dev_b, *dev_c;
int len = N*sizeof(int);
cudaMalloc((void**)&dev_a, len);
cudaMalloc((void**)&dev_b, len);
cudaMalloc((void**)&dev_c, len);
cudaMemcpy(dev_a, a, len, cudaMemcpyHostToDevice);
cudaMemcpy(dev_b, b, len, cudaMemcpyHostToDevice);
add<<<N,1>>>(dev_a, dev_b, dev_c);
cudaMemcpy(c, dev_c, len, cudaMemcpyDeviceToHost);
cudaFree(dev_a); cudaFree(dev_b); cudaFree(dev_c);
}
...and GPU
__global__ void add(int *a, int *b, int *c) {
int tid = blockIdx.x;
c[tid] = a[tid] + b[tid];
}

A simple example
}
__global__ void add(int *a, int *b, int *c) {
int tid = blockIdx.x;
c[tid] = a[tid] + b[tid];
}
a b c
+
++
===
+++
===
Language extension to
invoke our kernel with 'N'
blocks executing in parallel
one thread per block
Identify your block number
Add the single element whose
index matches your block number

A simple example – the effect at runtime
}
__global__ void add(int *a,...
c[2] = a[2] + b[2];
__g}lobal__ void add(int *a,...
c[1] = a[1] + b[1];
__g}lobal__ void add(int *a,...
c[0] = a[0] + b[0];
Grid of 'N' blocks
executing in parallel
}

Goal: Bringing GPU programming into Java
 There are times when you want this low level GPU control from Java
 Produce an API that reflects the concepts familiar in CUDA programming
 Make use of Java exceptions, automatic resource management, etc.
 Handle copying data to/from the GPU, flow of control from Java to GPU and back, etc
 Ability to invoke existing GPU module code from Java applications e.g. Thrust
CudaDevice – a CUDA capable GPU device
CudaBuffer – a region of memory on the GPU
CudaModule – user library of kernels to load into GPU
CudaKernel – for launching a device function
CudaFunction – a kernel's entry point
CudaEvent – for timing and synchronization
CudaException – for when something goes wrong
new Java APIs

Fundamental types in CUDA4J
CudaGrid
execution
engine
CuCduadBauBffuefrfer
CCuuddaaBBuufffeferr
PTX
.func add { … }
.func foo { … }
.func bar { … }
CudaDevice
CudaStream
add{}
CudaModule CudaKernel
Java CudaSurface
CudaDevice
CudaLinker
CudaEvent
CudaFunction
CudaTexture
CudaGlobal
CudaKernel
Used to combine multiple cubin/fatbin/PTXs
into single module
Device events
CudaModule
CudaFunction
foo{}
Corresponds to a HW feature in GPU
Relationship for generating an instance
Relationship as an argument
device
memory

first GPU device
native module
containing the kernel
grid of kernels that
will execute this task
move data from
Java heap to device
invoke the task
Explicit GPU vector addition in Java
move the result back
to the Java heap

Limitations and considerations
 Allows developers to code explicitly for the GPU
– These are new APIs that give close control of the device
– Uses familiar concepts and paradigms for GPU experts
– Convenience and productivity improvements from language
– Fundamental building blocks for higher level algorithms
 Requires the developer to identify suitable GPU workloads
– Re-code routines to operate on data in parallel
– Minimize branching flow of control in kernels
 Amortizing overhead of moving work to GPU
– Time taken to copy data between host and device over PCIe
– Overhead of switching flow of control from CPU to GPU
Flickr: Gareth Halfacree

Applying GPUs to Big Data problems
 Large retailers are collecting petabytes of information based on
customer transactions.
 Can we identify customers with similar behavior,
such that retailers can create customized products,
effective marketing campaigns, etc?
 Businesses that react quickly to changes in market segments
have an advantage.
 Challenge: Can we bring the power of GPUs to big data problems written in Java?
 Problem: Identify 20 – 25 year olds who are interested in smart phone products
 Solution: Put a K-means algorithm onto the GPU to identify clusters in large dataset
– Apache Mahout is a machine learning library for clustering and collaboration built on Apache Hadoop

K-means clustering – finding groups of data points
 Problem: Iteratively refine the location of 'K' loci, identifying clusters of 'N' data points, where
each data point has 'D' dimensions
 Approach: For each data point, which of the loci is closest?
– NP-hard problem requiring N x K x D independent operations
– Adjust K loci and iterate towards a stable answer
 The map computation can be parallelized (at a finer granularity than
managed by Apache Hadoop)
 A new GPU-enabled mapper class was created:
– batches of points are transferred from Java to a GPU for mapping
– a partial reduction is returned to the host
– a single Apache Mahout class was modified – to use our new mapper
class
Wikimedia: Chire

Mapper speed-up
 GPU vs. CPU
– Speed-up factor just doing the
map/reduce for different values
of K and D
– Baseline is standard Apache Mahout
implementation on CPU
– Comparison excludes Apache Hadoop
framework and I/O
Number of loci being considered
Speed-up multiplier for GPU compared to CPU
Number of variables per data point
 End-to-end speed-up solving the
problem was ~8x IBM Power 8 with Nvidia K40m GPU

GPU-enabling standard Java SE APIs
 Natural question after seeing the good speed-ups using explicit programming …
 What areas of the standard Java API implementation are suitable for off-loading onto GPU?
 We picked two candidates initially:
java.util.Arrays.sort(int[] a) and friends
– GPU modules exist that do efficient sorting
java.nio.charset.CharsetEncoder
– data-driven character set mapping of Strings
IBM Developer Kits for Java
ibm.com/java/jdk

GPU-enhanced sorting from Java – heuristics
 We employ heuristics that determine if the work
should be off-loaded to the GPU.
 Overhead of moving data to GPU, invoking
kernel, and returning results means small sorts
(<~20k elements) are faster on the CPU.
 Host may have multiple GPUs. Are any available
for the task?
 Is there space for conducting the sort on the
device?
Arrays.sort(myData);
Is the problem
large enough?
yes
Is there a
GPU currently
available?
yes
Is the
device
capable?
yes
no
no
no
Sort on GPU Sort on CPU

GPU-enabled array sort method
IBM Power 8 with Nvidia K40m GPU

GPU-enabled charset conversion
 Did not achieve speed-ups on charset encoding/decoding
 Cost of moving the data to/from GPU outweighed the benefit
 We have spent effort optimizing the CPU version with a number of JIT specials
 Rule of thumb on the system we have is that you need to
execute 'tens of instructions per data point' on the GPU
in order to see a benefit over the CPU
Flickr: rifqidahlgren

Beyond specific APIs – Java 8 streams
 Streams allow developers to express computation as aggregate parallel operations on data
 For example:
IntStream.range(0, N).parallel().forEach(i >
c[i] = a[i] + b[i]);
creates a stream whose operations can be executed in parallel
 What if we could recognize the terminal operation and conduct it on the GPU?
Reuses standard Java idioms, so no code changes required
No knowledge of GPU programming model required by the application developer
But no low-level manipulation of the device – the Java implementation has the controls
Future smarts introduced into the JIT do not require application code changes

JIT optimized GPU acceleration
 As the JIT compiles a stream expression we can identify candidates for GPU off-loading
– Arrays copied to and from the device implicitly
– Java operations mapped to GPU kernel operations
– Preserves the standard Java syntax and semantics bytecodes
 Early steps
– Recognize a limited set of operations within the lambda expressions,
• notably no object references maintained on GPU
– Default grid dimensions and operating parameters for the GPU
workload
– Redundant/pessimistic data transfer between host and device
• Not using GPU shared memory
– Limited heuristics about when to invoke the GPU and when to
generate CPU instructions
intermediate
representation
optimizer
code generator code generator
CPU native PTX ISA
CPU GPU

JIT / GPU optimization of Lambda expression
JIT recognized Java code for matrix
multiplication using Java 8 parallel stream
Speed-up factor when run on a GPU enabled host
IBM Power 8 with Nvidia K40m GPU

Conclusions
✔ GPUs provide a powerful co-processor suited to handling large amounts of data
✔Workloads need to be carefully chosen to exploit the GPU's specific capabilities
✔ Custom APIs make the GPU accessible to Java application developers
✔ IBM's Java implementation is introducing heuristics to off-load calls to standard
library APIs from CPU to GPU
✔ JIT optimization of user code will increasingly move your application code to
custom co-processors, including GPU
✔We have seen good speed-ups of around 8x to 10x with real world Big Data
applications running on GPU devices

http://ibm.biz/javaone2014

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© IBM Corporation 2014. All Rights Reserved.
IBM, the IBM logo, and ibm.com are trademarks or registered trademarks of International
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Other product and service names might be trademarks of IBM or other companies.
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Using GPUs to Handle Big Data with Java

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