Streaming-oriented parallelization of domain-independent irregular kernels

Authors:
Jacobo Lobeiras;Margarita Amor;Manuel Arenaz;Basilio B. Fraguela
Affiliations:
Computer Architecture Group, University of A Coruña, Spain;Computer Architecture Group, University of A Coruña, Spain;Computer Architecture Group, University of A Coruña, Spain;Computer Architecture Group, University of A Coruña, Spain
Venue:
Euro-Par 2010 Proceedings of the 2010 conference on Parallel processing
Year:
2010

Citing 8
Cited 0

Array SSA form and its use in parallelization

POPL '98 Proceedings of the 25th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Data partitioning-based parallel irregular reductions: Research Articles

Concurrency and Computation: Practice & Experience - Compilers for Parallel Computers
Stream Programming on General-Purpose Processors

Proceedings of the 38th annual IEEE/ACM International Symposium on Microarchitecture
Automated and accurate cache behavior analysis for codes with irregular access patterns: Research Articles

Concurrency and Computation: Practice & Experience - Current Trends in Compilers for Parallel Computers (CPC2006)
XARK: An extensible framework for automatic recognition of computational kernels

ACM Transactions on Programming Languages and Systems (TOPLAS)
OpenMP to GPGPU: a compiler framework for automatic translation and optimization

Proceedings of the 14th ACM SIGPLAN symposium on Principles and practice of parallel programming
An inspector-executor algorithm for irregular assignment parallelization

ISPA'04 Proceedings of the Second international conference on Parallel and Distributed Processing and Applications
Automatic C-to-CUDA code generation for affine programs

CC'10/ETAPS'10 Proceedings of the 19th joint European conference on Theory and Practice of Software, international conference on Compiler Construction

Quantified Score

Hi-index	0.00

Visualization

Abstract

Current parallelizing and optimizing compilers use techniques for the recognition of computational kernels to improve the quality of the target code. Domain-independent kernels characterize the computations carried out in an application, independently of the implementation details of a given programming language. This paper presents streaming-oriented parallelizing transformations for irregular assignment and irregular reduction kernels. The advantage of these code transformations is that they enable the parallelization of many algorithms with little effort without a depth knowledge of the particular application. The experimental results show the efficiency on current GPUs, although the main goal of the proposed techniques is not performance, but assist the programmer in the parallelization for a better productivity.