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GPUs: Overview of Architecture and
Programming Options
Lee Barford
firstname dot lastname at gmail dot com
Outline
Why parallel computing is now important
What GPUs are and what they provide
Overview of GPU architecture
• Enough to orient the discussion of programming them
• Future changes
Three “languages” for programming GPUs
• Those we’re not doing include CUDAFortran, Python CUDA & CL bindings, WebCL
3
Graph from UC Berkeley ParLab
Serial AppPerformance
Exponentially growing gap
4
Graphics Processor (GPU) as Parallel Accelerator
• Commodity priced, massively parallel floating point
• Claimed performance on various problems 50-2500x CPU running serial code
Graph from http://drdobbs.com/high-performance-computing/231500166
The GPU as a Co-Processor to the CPU:The physical and logical connections
Main memory
chipset
GPU memory
PCIe
Slow
Control actions & code (kernels) to run
I/Os:• Video• Ethernet• USB hub• Firewire• …
CPU
GPU
Running GPU code is like requesting asynchronous I/O
0.5-3 years from now: Fusion of CPU and GPU
CPU
Main memory I/O subsystem
Multiple cores
GPU
Running GPU code will be like pending method pointers for future execution. (Like C++11, TBB, TPL, PPL).
Hardware task scheduler
Programming Tomorrow’s CPU will be Like Programming Today’s GPU
• GPUs that compute will come “for free” with computers
• Slow step of moving data to/from GPU will be eliminated
• Hardware task scheduler for both CPU and GPU will
• Almost eliminate OS & I/O overhead for invoking GPU kernels
• Also almost eliminate OS overhead for invoking parallel tasks on CPU
• AMD laptop chip available now (but no boards/systems)
• NVIDIA GPU+ARM chip available now for battery operated devices
• Both promise desktop chips in next year or two
• Programming models will probably evolve from what we’ll cover
• Course will use current, PCIe-based GPUs
• We will be dealing with overheads that will pass away over next few years
CUDA (NVIDIA) GPU Compute Architecture:Many Simple, Floating-Point Cores
32 cores (Streaming Multiprocessor) share:
• Instruction stream
• Registers
• Execute same program (kernel)
• SPMD: ~ [Same place in same kernel at the same time]
• Act as 100-1000’s more cores by switching context instead of waiting for memory
1000’s of virtual cores executing same lines of code together, but
Sharing limited resources
Cores organized into groups
GPU has multiple SMs
• SMs run in parallel
• Do not need to be executing same location in the same program at the same time
• In aggregate, many 1000’s of parallel copies of same kernel running simultaneously
• Total of up to 1Tflop/s at peak
CENTRAL SOFTWARE ISSUE:
• How to generate and control this much parallelism
GPUs: Programming Options
• Libraries: called from CPU code. Write no GPU code. Examples:
• Image/video processing, dense & sparse matrix, FFT, random numbers
• Generic programming for GPU
• Thrust
• Like C++ Standard Template Library
• Specialize & use built-in data structures and algorithms
• NVIDIA GPUs only
• Programming the GPU directly
• CUDA C/C++, OpenCL, WebCL, CUDA Fortran, various Python libraries
• Write code that runs on GPU (kernels)
• Write CPU code that directly controls and coordinates
– Data movement between CPU memory and GPU memory
– Startup of kernels on GPU
– CPU processing of results from GPU when they become available
CUDA C/C++ vs OpenCL
CUDA C/C++• Proprietary (NVIDIA)
• Code runs on NVIDIA GPUs
• Reportedly 10-50% faster than OpenCL
• Compiles at build time to binary code for particular targeted hardware
• Specific NVIDIA hardware architecture versions
• No compiler available at run time
OpenCL• Open standard (Khronos)
• Code runs on NVIDIA & AMD GPUs, x86 multicore, FPGAs (academic research) at the same time
• Compiles at build time to intermediate form that is compiled at run time for the hardware that is present
• Compiler is available at run time
• Can execute downloaded or dynamically generated source code
The Three Programming Environments We’ll Cover
OpenCL:• Write once, run many• Supports heterogeneous parallel machines (fusion)• Tool chains good enough for research• IMHO, will eventually replace CUDA C/C++
CUDA C/C++:• Very efficient code• Lots of fussy detail to get that efficiency• Robust tool chains for Linux, Windows, MacOS• Specific to NVIDIA
Thrust:• Easy to write• Algorithms provided among the fastest (e.g., sort)• NVIDIA GPUs only
Class Project Idea
• Accurate edge finding in a 1D signal
• Journal paper published on multicore version
• Student project last year doing Thrust implementation
• Project: Do CUDA version + performance tests
• Paper combining previous student’s work with above: 60% probability of getting accepted in a particular IEEE conference
• 3 co-authors, including previous student & Lee
• Extended abstract due: Nov 6
• Class project due during finals, same as everyone else
• Camera ready paper due: March 4
• See or email me in the next week or two if interested
Questions