Points-to analysis in almost linear time
POPL '96 Proceedings of the 23rd ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Super-Scalar RAM-CPU Cache Compression
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
A parallel hybrid banded system solver: the SPIKE algorithm
Parallel Computing - Parallel matrix algorithms and applications (PMAA'04)
Accelerating sparse matrix computations via data compression
Proceedings of the 20th annual international conference on Supercomputing
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
Compiler Analysis of the Value Ranges for Variables
IEEE Transactions on Software Engineering
Optimization of sparse matrix-vector multiplication on emerging multicore platforms
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Optimizing sparse matrix-vector multiplication using index and value compression
Proceedings of the 5th conference on Computing frontiers
Analyzing memory access intensity in parallel programs on multicore
Proceedings of the 22nd annual international conference on Supercomputing
Extendable pattern-oriented optimization directives
CGO '11 Proceedings of the 9th Annual IEEE/ACM International Symposium on Code Generation and Optimization
Extendable pattern-oriented optimization directives
ACM Transactions on Architecture and Code Optimization (TACO)
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This paper proposes a compiler-automated array compression scheme to reduce the memory bandwidth consumption of programs and thereby to improve their execution speed. Three encoding methods are developed for such compression. Formulas are derived to analyze the cost and benefit of such methods. To ease the programmer's effort for writing and maintaining complex source code that utilizes compression, we implement our technique in a compiler which automatically transforms the program into different versions corresponding to different encoding methods. The compiler also inserts operations to adaptively invoke the preferred version at run time, including the original version which performs no compression. Results show that our compiler-automated adaptive scheme improves the execution speed over the original version by an average of 9% for a set of benchmark programs which perform memory-intensive sparse matrix-vector multiplications (SpMV). These results take into account of overhead to make the adaptive decision. When tested separately, the individual encoding methods speed up program execution by as high as 41%, which compares favorably against previous compression methods manually applied to SpMV.