A characterization of the Rodinia benchmark suite with comparison to contemporary CMP workloads

Che, Shuai; Sheaffer, Jeremy W.; Boyer, Michael; Szafaryn, Lukasz G.; Wang, Liang; Skadron, Kevin

doi:10.1109/iiswc.2010.5650274

Cited by 235 publications

(115 citation statements)

References 28 publications

(61 reference statements)

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“…Thus, the heterogeneous versions of HS shows reduced performance. LUD involves triangular matrix operations and has many row-wise and column-wise dependencies [7]. The heterogeneous versions make a lot of non-coalesced accesses to the device memory since the device cache or shared memory is not big enough to hold the entire matrix involved in the computation, which leads to reduced performance in GPU computation.…”

Section: Performance Of Rodinia Benchmarksmentioning

confidence: 99%

“…In the experimental section, we used the Rodinia benchmark suite 2.0.1 [6,7]. Out of the 16 benchmarks available in the Rodinia benchmark suite, we have used 15.…”

Section: Benchmarksmentioning

confidence: 99%

“…For each benchmark, the suite provides two versions -an OpenMP version and an optimized CUDA version [6,7]. One heterogeneous version uses OpenACC directives (Cray compiler), while the other uses the PGI programming model directives (PGI compiler).…”

Section: Performance Of Rodinia Benchmarksmentioning

confidence: 99%

“…First, a micro-benchmark suite that we developed for this project and the other is the Rodinia benchmark suite 2.0.1 [6,7]. Our micro-benchmark suite consists of a set of loops to assess the ability of the compilers to perform standard loop transformations for exposing parallelism.…”

Section: Benchmarksmentioning

confidence: 99%

“…In this paper we evaluate the Rodinia v2.0.1. benchmark suite [6,7], which contains regular and irregular applications from different domains. Also, this paper evaluates two of the three currently available commercial compilers (CAPS is the only other commercial compiler that we did not consider in this work).…”

Section: Related Workmentioning

confidence: 99%

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Directive-Based Compilers for GPUs

Ghike

Tejero

Garzarán

et al. 2015

Languages and Compilers for Parallel Computing

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Abstract. General Purpose Graphics Computing Units can be effectively used for enhancing the performance of many contemporary scientific applications. However, programming GPUs using machine-specific notations like CUDA or OpenCL can be complex and time consuming. In addition, the resulting programs are typically fine-tuned for a particular target device. A promising alternative is to program in a conventional and machine-independent notation extended with directives and use compilers to generate GPU code automatically. These compilers enable portability and increase programmer productivity and, if effective, would not impose much penalty on performance. This paper evaluates two such compilers, PGI and Cray. We first identify a collection of standard transformations that these compilers can apply. Then, we propose a sequence of manual transformations that programmers can apply to enable the generation of efficient GPU kernels. Lastly, using the Rodinia Benchmark suite, we compare the performance of the code generated by the PGI and Cray compilers with that of code written in CUDA. Our evaluation shows that the code produced by the PGI and Cray compilers can perform well. For 6 of the 15 benchmarks that we evaluated, the compiler generated code achieved over 85% of the performance of a hand-tuned CUDA version.

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Section: Performance Of Rodinia Benchmarksmentioning

confidence: 99%

“…In the experimental section, we used the Rodinia benchmark suite 2.0.1 [6,7]. Out of the 16 benchmarks available in the Rodinia benchmark suite, we have used 15.…”

Section: Benchmarksmentioning

confidence: 99%

Section: Performance Of Rodinia Benchmarksmentioning

confidence: 99%

Section: Benchmarksmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Directive-Based Compilers for GPUs

Ghike

Tejero

Garzarán

et al. 2015

Languages and Compilers for Parallel Computing

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Autotuning CUDA compiler parameters for heterogeneous applications using the OpenTuner framework

Bruel

Amarís

Goldman

2017

Concurrency and Computation

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A Graphics Processing Unit (GPU) is a parallel computing coprocessor specialized in accelerating vector operations. The enormous heterogeneity of parallel computing platforms justifies and motivates the development of automated optimization tools and techniques. The Algorithm Selection Problem consists in finding a combination of algorithms, or a configuration of an algorithm, that optimizes the solution of a set of problem instances. An autotuner solves the Algorithm Selection Problem using search and optimization techniques.In this paper, we implement an autotuner for the Compute Unified Device Architecture compiler's parameters using the OpenTuner framework. The autotuner searches for a set of compilation parameters that optimizes the time to solve a problem. We analyze the performance speedups, in comparison with high-level compiler optimizations, achieved in three different GPU devices, for 17 heterogeneous GPU applications, 12 of which are from the Rodinia Benchmark Suite. The autotuner often beats the compiler's high-level optimizations, but underperformed for some problems. We achieved over 2x speedup for Gaussian Elimination and almost 2x speedup for Heart Wall, both problems from the Rodinia Benchmark, and over 4x speedup for a matrix multiplication algorithm. problem defines a search space. Various optimization techniques search this space, guided by the performance metrics, for the algorithm or configuration that best solve the problem.There are specialized autotuners for domains such as matrix multiplication [2], dense [3], or sparse [4] matrix linear algebra and parallel programming [5]. Other autotuning frameworks provide more general tools for the representation and search of program configurations, enabling the implementation of autotuners for different problem domains [6,7].The OpenTuner framework [6] provides tools for the implementation of autotuners for various problem domains. It implements different search techniques that explore the same search space for program optimizations. Running and measuring program execution time -that is, the empirical exploration of the search space -is done sequentially. The framework also provides support for parallel compilation.In this paper, we implemented an autotuner for the CUDA compiler using the use the OpenTuner framework [6] and used it to search for the compilation parameters that optimize the performance of 17 heterogeneous GPU applications, 12 of which are from the Rodinia Benchmark Suite [8]. We used three different NVIDIA GPUs in the experiments, the Tesla K40, the GTX 980, and the GTX 750.Our main contribution is to show that it is possible to optimize code written for GPUs by automatically tuning just the parameters of the CUDA compiler. We propose a thorough methodology for analyzing result correctness and checking for invalid flag combinations and compilation errors. The optimization achieved by autotuning often beats the compiler high-level optimization options, such as -O1, -O2, and -O3. The autotuner found compilation options that achieved...

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Autotuning of configuration for program execution in GPUs

Balaiah

Parthasarathi

2019

Concurrency and Computation

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Summary Graphics Processing Units (GPUs) are used as accelerators for improving performance while executing highly data parallel applications. The GPUs are characterized by a number of Streaming Multiprocessors (SM) and a large number of cores within each SM. In addition to this, a hierarchy of memories with different latencies and sizes is present in the GPUs. The program execution in GPUs is thus dependent on a number of parameter values, both at compile time and runtime. To obtain the optimal performance with these GPU resources, a large parameter space is to be explored, and this leads to a number of unproductive program executions. To alleviate this difficulty, machine learning–based autotuning systems are proposed to predict the right configuration using a limited set of compile‐time parameters. In this paper, we propose a two‐stage machine learning–based autotuning framework using an expanded set of attributes. The important parameters such as block size, occupancy, eligible warps, and execution time are predicted. The mean relative error in prediction of different parameters ranges from of 16% to 6.5%. Dimensionality reduction for the features set reduces the features by up to 50% with further increase in prediction accuracy.

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A characterization of the Rodinia benchmark suite with comparison to contemporary CMP workloads

Cited by 235 publications

References 28 publications

Directive-Based Compilers for GPUs

Directive-Based Compilers for GPUs

Autotuning CUDA compiler parameters for heterogeneous applications using the OpenTuner framework

Autotuning of configuration for program execution in GPUs

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