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GPU Programming with 
Haskell 
Steve Severance 
sseverance@alphaheavy.com
Outline 
Introduction to GPUs 
When to use a GPU instead of a CPU 
Using a GPU with accelerate 
Building an options pricer
What is a GPU? 
Graphics Processing Unit 
Hundreds or Thousands of Cores 
High Memory Throughput 
Fully Programmable
GPU Architecture 
Single Instruction Multiple Data (SIMD) 
High Throughput Thread Scheduler 
Interleaving Operations
GPU Architecture 
16GB/s 
GPU CPU Memory
GPU Circa 1999 
Geforce 256 
Accelerated Graphics Port (AGP) 
Hardware Transform and Lighting (TnL) 
Fixed Function Pipeline
GPU Circa 2001 
Geforce 3/R200/XBox 
First Pixel/Vertex Shaders 
Limited C-like Language
GPU Circa 2014 
Fully Programmable 
Unified Memory 
Rich High Level Languages/Tools
GPU Tradeoffs 
Limited branching 
Limited Memory 
High Latency
GPU vs CPU 
GPU is about throughput 
CPU is about flexibility and latency
Programmability 
CUDA 
OpenCL 
DirectCompute
GPU Problems 
Non-branching algorithms 
Matrix (cudaBLAS) 
Deep Learning 
Options Pricing
Can I run GPU Programs? 
accelerate requires CUDA 
OpenCL is a low level OpenCL wrapper 
NVidia CUDA Tools (https://developer.nvidia.com/ 
cuda-toolkit)
Introducing Accelerate 
DSL for Parallel Code 
Primarily CUDA, Also LLVM 
Compiler lowers into CUDA code
Accelerate Basics 
Acc is our DSL type. Holds the Abstract Syntax Tree 
(AST) of our computation 
Familiar operators replace Prelude (fold,map,zip,etc…)
Accelerate Basics 
Creating a Computation 
Acc (Array DIM1 Float) -> Acc (Array DIM1 Float) 
Running a Computation 
run :: Arrays a => Acc a -> a
Arrays 
data Array sh e 
Comprised of both a Shape and an Element (Elt) 
Elt instances for common numeric types and tuples 
Arrays can be multi-dimensional, but not nested
Array Shapes 
Z is a Rank-0 
:. Operator Increases the Rank by One Dimension 
DIM1, DIM2, DIM3, etc…
Computations 
Acc is a computation on an array 
Exp is a computation on an element 
Exp can also be used to pass constants
What run is going to do 
Compile our Program 
Copy Data to GPU 
Execute Program 
Copy Results Back to Memory
Black-Sholes 
Partial Differential Equation to Compute the Price of an 
Option 
Massive Performance Boost on a GPU 
Bloomberg Uses GPUs to compute Options Prices
Black-Sholes Equation 
Stolen from investopedia.com
Code/Demo Time
Summary 
lift/unlift 
use adds an Array to the computation 
constant wraps constants 
map does what map always does
What next? 
accelerate has a rich API 
Slices 
Aggregation 
Recursion 
Stencils
Thanks 
Nathan Howell 
The accelerate Team 
You for listening
Further Reading 
https://speakerdeck.com/tmcdonell/gpgpu-programming- 
in-haskell-with-accelerate 
http://hackage.haskell.org/package/accelerate 
http://quantlib-gpu.sourceforge.net/ 
AcceleratingFinancialApplicationsOnTheGPU-paper.pdf

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