Redundancy elimination revisited

Cooper, Keith D.; Eckhardt, Jason; Kennedy, Ken

doi:10.1145/1454115.1454120

Cited by 21 publications

(16 citation statements)

References 15 publications

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“…Global reassociation reorders the operands of a commutative expression to promote better redundancy elimination. The idea is similar to the reassociation algorithm in Enhanced Scalar Replacement [13], but our algorithm is more compiletime efficient -its time complexity is linear in program size.…”

Section: Global Reassociationmentioning

confidence: 99%

gpucc: an open-source GPGPU compiler

Belevich

Bendersky

et al. 2016

Proceedings of the 2016 International Symposium on Code Generation and Optimization

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Graphics Processing Units have emerged as powerful accelerators for massively parallel, numerically intensive workloads. The two dominant software models for these devices are NVIDIA's CUDA and the cross-platform OpenCL standard. Until now, there has not been a fully open-source compiler targeting the CUDA environment, hampering general compiler and architecture research and making deployment difficult in datacenter or supercomputer environments. In this paper, we present gpucc, an LLVM-based, fully open-source, CUDA compatible compiler for high performance computing. It performs various general and CUDAspecific optimizations to generate high performance code. The Clang-based frontend supports modern language features such as those in C++11 and C++14. Compile time is 8% faster than NVIDIA's toolchain (nvcc) and it reduces compile time by up to 2.4x for pathological compilations (>100 secs), which tend to dominate build times in parallel build environments. Compared to nvcc, gpucc's runtime performance is on par for several open-source benchmarks, such as Rodinia (0.8% faster), SHOC (0.5% slower), or Tensor (3.7% faster). It outperforms nvcc on internal large-scale end-to-end benchmarks by up to 51.0%, with a geometric mean of 22.9%.

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Section: Global Reassociationmentioning

confidence: 99%

gpucc: an open-source GPGPU compiler

Belevich

Bendersky

et al. 2016

Proceedings of the 2016 International Symposium on Code Generation and Optimization

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“…In both examples, the redundancies require large-scope (across multiple levels of loops) computation reordering to detect and remove. As noticed in numerous studies [Cooper et al 2008;Deitz et al 2001;Ding et al 2017Ding et al , 2015Drake and Hamerly 2012;Elkan 2003;Fahim et al 2006;Goldberg and Harrelson 2005;Greenspan et al 2000;Gupta and Rajopadhye 2006;Gutman 2004;Hamerly 2010;Ngai et al 2006;Wang et al 2012;Wang 2011], such large-scope loop redundancies are common, especially in applications in computational physics, chemistry, data analytics, and other domains that involve relatively complex formulae or algorithms. When translating those complex formulae or algorithms into computer programs, the developers often intuitively follow the formulae or algorithms step by step, producing logically easy-to-understand and practically easy-to-maintain code rather than trying to minimize redundant computations.…”

Section: Introductionmentioning

confidence: 98%

GLORE: generalized loop redundancy elimination upon LER-notation

Ding

Shen

2017

Proc. ACM Program. Lang.

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This paper presents GLORE, a novel approach to enabling the detection and removal of large-scope redundant computations in nested loops. GLORE works on LER-notation, a new representation of computations in both regular and irregular loops. Together with a set of novel algorithms, it makes GLORE able to systematically consider computation reordering at both the expression level and the loop level in a unified manner. GLORE shows an applicability much broader than prior methods have, and frequently lowers the computational complexities of some nested loops that are elusive to prior optimization techniques, producing significantly larger speedups. CCS Concepts: • Software and its engineering → General programming languages;

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“…Unlike the previous techniques that only address the inner-loop locality (or redundancy) [1][2][3][4], our algorithm can detect inter-loop locality and eliminate the computation redundancy across loops with multiple dimensions.…”

Section: Introductionmentioning

confidence: 99%

Optimizing stencil code via locality of computation

Luo

Tan

2014

Proceedings of the 23rd International Conference on Parallel Architectures and Compilation

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Stencil computation is a performance critical kernel used in scientific and engineering applications. We define a term of locality of computation to guide stencil optimization by either architecture or compiler. Being analogous to locality of reference, computational behavior is also classified into spatial locality and temporal locality. This paper develops equivalent computation elimination (ECE) approach in multi-level loop for exploiting temporal locality of computation. The strength of ECE lies on an intermediate-based searching algorithm to eliminate inter-iteration computational redundancies of all possible combination and a multiple dimensions replacement algorithm to replace redundant computation across loops of multiple dimensions. We implemented ECE in ROSE compiler infrastructure. The experiment shows that ECE improves performance by 20% on average due to the consciousness of temporal locality.

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Redundancy elimination revisited

Cited by 21 publications

References 15 publications

gpucc: an open-source GPGPU compiler

gpucc: an open-source GPGPU compiler

GLORE: generalized loop redundancy elimination upon LER-notation

Optimizing stencil code via locality of computation

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