| Audience |
This tutorial is targeted primarily at application developers, computer/computational scientists, and graduate students interested in programming models and/or compiler optimization issues for GPGPU computing. Knowledge of C programming will be assumed; basic knowledge of processor architectures will be assumed; no prior parallel programming experience or familiarity with source-to-source transformations will be assumed.
| Brief Description |
GPU based parallel computing is of tremendous interest today because of their significantly higher peak performance than general-purpose multicore processors, as well as better energy efficiency. However, harnessing the power of GPUs is more complicated than general-purpose multi-cores. There has been considerable recent interest in two complementary approaches to assist application developers:
This tutorial will provide an introductory survey covering both these aspects.
- programming models that explicitly expose the programmer to parallelism; and
- compiler optimization and tuning frameworks to automatically transform programs for parallel execution on GPUs.
| Lecture Outline |
- Introduction
- CPUs versus GPUs
- Programming Models for GPUs
- Compiler Transformations for GPUs
- GPU Architectures and programming
- GPU architectures
- General-purpose computation on GPUs
- Programming models and idioms
- GPU programming models/environments:
- OpenCL
- CUDA
- CAL
- PGI Accelerator
- CAPS/HMPP
- Code examples on GPUs
- Examples of CPU vs. GPU performance
- Compiler optimizations and tuning for GPUs
- Performance characterization
- Optimizing memory accesses
- Multi-level parallelism exploitation
- Performance models and emipirical search
- Compiler-driven tuning
- Examples of application optimization
- Software managed memory hierarchies
| Tutorial Speakers |
J. (Ram) Ramanujam received the B. Tech. degree in Electrical Engineering from the Indian Institute of Technology, Madras, India in 1983, and his M.S. and Ph. D. degrees in Computer Science from The Ohio State University in 1987 and 1990 respectively. He is currently the John E. and Beatrice L. Ritter Distinguished Professor in the Department of Electrical and Computer Engineering at Louisiana State University (LSU). In addition, he holds a joint faculty appointment with the LSU Center for Computation and Technology. His research interests are in compilers and runtime systems for high-performance computing, domain-specific languages and compilers for parallel computing, embedded systems, and high-level hardware synthesis. He has participated in several NSF-funded projects including the Tensor Contraction Engine and the Pluto project for automatic parallelization. Additional details can be found at http://www.ece.lsu.edu/jxr/.
P. (Saday) Sadayappan received the B. Tech. degree from the Indian Institute of Technology, Madras, India, and an M.S. and Ph. D. from the State University of New York at Stony Brook, all in Electrical Engineering. He is currently a Professor in the Department of Computer Science and Engineering at The Ohio State University. His research interests include compiler/runtime optimization for parallel computing, and domain-specific languages for high-performance scientific computing. He has led several NSF-funded projects including the Tensor Contraction Engine and the Pluto project for automatic parallelization. Additional details can be found at http://www.cse.ohio-state.edu/~saday/.