OpenCUDA+MPI - A Framework for Heterogeneous GP ... - GitHub
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OpenCUDA+MPI - A Framework for Heterogeneous GP ... - GitHub
CUDA. Compute Unified Device Architecture. Established interface with (nVidia) GPU's. pyCUDA. Deferred CUDA kernel compi
OpenCUDA+MPI A Framework for Heterogeneous GP-GPU Cluster Computing
Kenny Ballou
March 20, 2013
Ballou OpenCUDA+MPI
1 GP-GPU Computing Introduction 2 Problems and Solutions 3 A Little About Methodology 4 What’s Been Done
Ballou OpenCUDA+MPI
1 GP-GPU Computing Introduction
Parallel versus Distributed Computing Applications of Supercomputing Who Uses Supercomputing? 2 Problems and Solutions 3 A Little About Methodology 4 What’s Been Done
Ballou OpenCUDA+MPI
Introduction Parallel and Distributed Computing
What is GP-GPU Distributed Computing? Parallel: Processing concurrently
Distributed: Processing over many computers, typically in parallel, but not always Local Grid Computing
Ballou OpenCUDA+MPI
Applications of Supercomputing What can we do with Parallel and Distributed Computing?
Solving (Large) Linear Systems LINPACK Benchmarks
Fluid Dynamic Simulations N-Body Simulations Brute-Force Password/Hash Cracking Prime Number Searching Protein Folding Image Analysis / Manipulation ...
Ballou OpenCUDA+MPI
Who Uses Supercomputing?
ALL The People
Ballou OpenCUDA+MPI
Who Uses Supercomputing? No Really. . .
Google – Page Indexing Created Map-Reduce
Facebook – Data Mining Universities Many Others
Ballou OpenCUDA+MPI
1 GP-GPU Computing Introduction 2 Problems and Solutions
Problems with Current Solutions Solutions Plans and Goals 3 A Little About Methodology 4 What’s Been Done
Ballou OpenCUDA+MPI
Problems
”Distributed Programming” is expensive Specificity of Hardware Data Distribution Volume NFS
Fault Tolerance Optimizing Resources and Utilization
Ballou OpenCUDA+MPI
A Framework Solutions
Ease Programming Interface for Highly Parallel Distributed Computing Allow for Diversity in Computing Environment Bring together ideas from both types of distributed computing ”Jungle Computing”
Ballou OpenCUDA+MPI
Plan and Goals
Develop a framework for distributed computing over a heterogeneous cluster Develop several different solutions for vascular extraction from CT angiography scans Profile the different solutions Add Cluster/ Node Configuration and Scheduling Options Release as FOSS to the world
Ballou OpenCUDA+MPI
1 GP-GPU Computing Introduction 2 Problems and Solutions 3 A Little About Methodology
Implementation Details 4 What’s Been Done
Ballou OpenCUDA+MPI
Implementation Details
Arch Linux Salt Python CUDA (Open)MPI
Ballou OpenCUDA+MPI
Arch Linux
Core Tenet: Minimalism Small Lightweight Familiarity
Ballou OpenCUDA+MPI
Salt More than just NaCl
Provisioning tool for managing infrastructure Allows for ”Push” based state changes Remote Execution Simplicity Fast
Ballou OpenCUDA+MPI
Python
Development Speed: Expressive and Readable Fast Enough Written in C/C++
Great and Many Profiling Tools time.time() timeit cProfile ...
Where slow, allows use of C/C++ code
Ballou OpenCUDA+MPI
CUDA Compute Unified Device Architecture
Established interface with (nVidia) GPU’s pyCUDA Deferred CUDA kernel compilation
Familiarity
Ballou OpenCUDA+MPI
(Open)MPI
Established interface for inter-process communication mpi4py One of the most complete MPI implementations
Ballou OpenCUDA+MPI
1 GP-GPU Computing Introduction 2 Problems and Solutions 3 A Little About Methodology 4 What’s Been Done
What I have been doing Moving Forward Problems Encountered Potential Solutions
Ballou OpenCUDA+MPI
Tasks
Learning MPI, mpi4py, and pyCUDA Node/ Cluster Administration Node Build Scripts Salt Configuration Special Thanks to Danny
Lots of thinking
Ballou OpenCUDA+MPI
Moving Forward
Finish Creating Salt States and Configuration Continue Learning MPI and pyCUDA Develop Framework
Ballou OpenCUDA+MPI
Problems / Roadblocks
Power Requirements NFS Share /home performance Time?
Ballou OpenCUDA+MPI
Potential Solutions
Request(ing) more suitable and stable power Researching Distributed Filesystems / File storage
Ballou OpenCUDA+MPI
OpenCUDA+MPI A Framework for Heterogeneous GP-GPU Cluster Computing
