Analyzing Programming Effort Model Accuracy of High-Level Parallel Programs for Stream Processing

Andrade, Gabriella; Griebler, Dalvan; Santos, Rodrigo Pereira dos; Keßler, Christoph; Ernstsson, August; Fernandes, Luiz Gustavo

doi:10.1109/seaa56994.2022.00043

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Most related works use such metrics, as discussed in Section 2. However, these metrics may be inaccurate and lead users to wrong assumptions [18].…”

Section: Programmability Evaluationmentioning

confidence: 99%

“…The bases of the method is on the observation that the complexity of a program is related to the number of unique operators and operands used in it. The application of Halstead method is to predict the effort required to develop or maintain a software program and to estimate the number of bugs that may be present in the program [18]. We also can use the method to compare the complexity of different programs and to identify sections of code that may be particularly difficult to understand or maintain.…”

Section: Programmability Evaluationmentioning

confidence: 99%

“…Halstead's method, however, does not correctly address parallel programming since it can not recognize specific code tokens from most of the PPIs. Andrade et al [18] overcame this problem and adapted Halstead's method to support some PPIs, including the ones used in this work. This method is based on tokens of code, classified as operators or operands, and proposes a series of measures, including estimated development time.…”

Section: Programmability Evaluationmentioning

confidence: 99%

“…This method is based on tokens of code, classified as operators or operands, and proposes a series of measures, including estimated development time. [18] added specific tokens from different PPIs and developed a tool called PHalstead 1 from this.…”

Section: Programmability Evaluationmentioning

confidence: 99%

See 3 more Smart Citations

Performance and Programmability of GrPPI for Parallel Stream Processing on Multi-Cores

Garcia,

Griebler,

Schepke

et al. 2023

Preprint

Self Cite

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GrPPI library aims to simplify the burdening task of parallel programming. It provides a unified, abstract, and generic layer while promising minimal overhead on performance. Although it supports stream parallelism, GrPPI lacks an evaluation regarding representative performance metrics for this domain, such as throughput and latency. This work evaluates GrPPI focused on parallel stream processing. We compare the performance, memory usage, and programmability of GrPPI against handwritten parallel code. For this, we use the benchmarking framework SPBench to build custom GrPPI benchmarks and benchmarks with handwritten parallel code using the same backends supported by GrPPI. The basis of the benchmarks is real applications, such as Lane Detection, Bzip2, Face Recognizer, and Ferret. Experiments show that while performance is competitive with handwritten code in many cases, the infeasibility of fine-tuning GrPPI is a crucial drawback. Despite this, programmability experiments estimate that GrPPI has the potential to reduce by about three times the development time of parallel applications.

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“…Most related works use such metrics, as discussed in Section 2. However, these metrics may be inaccurate and lead users to wrong assumptions [18].…”

Section: Programmability Evaluationmentioning

confidence: 99%

Section: Programmability Evaluationmentioning

confidence: 99%

Section: Programmability Evaluationmentioning

confidence: 99%

Section: Programmability Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Performance and Programmability of GrPPI for Parallel Stream Processing on Multi-Cores

Garcia,

Griebler,

Schepke

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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Assessing Application Efficiency and Performance Portability in Single-Source Programming for Heterogeneous Parallel Systems

Ernstsson

Griebler

Keßler

2022

Int J Parallel Prog

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We analyze the performance portability of the skeleton-based, single-source multi-backend high-level programming framework SkePU across multiple different CPU–GPU heterogeneous systems. Thereby, we provide a systematic application efficiency characterization of SkePU-generated code in comparison to equivalent hand-written code in more low-level parallel programming models such as OpenMP and CUDA. For this purpose, we contribute ports of the STREAM benchmark suite and of a part of the NAS Parallel Benchmark suite to SkePU. We show that for STREAM and the EP benchmark, SkePU regularly scores efficiency values above 80% and in particular for CPU systems, SkePU can outperform hand-written code.

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Performance and programmability of GrPPI for parallel stream processing on multi-cores

Garcia,

Griebler,

Schepke

et al. 2024

J Supercomput

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GrPPI library aims to simplify the burdening task of parallel programming. It provides a unified, abstract, and generic layer while promising minimal overhead on performance. Although it supports stream parallelism, GrPPI lacks an evaluation regarding representative performance metrics for this domain, such as throughput and latency. This work evaluates GrPPI focused on parallel stream processing. We compare the throughput and latency performance, memory usage, and programmability of GrPPI against handwritten parallel code. For this, we use the benchmarking framework SPBench to build custom GrPPI benchmarks and benchmarks with handwritten parallel code using the same backends supported by GrPPI. The basis of the benchmarks is real applications, such as Lane Detection, Bzip2, Face Recognizer, and Ferret. Experiments show that while performance is often competitive with handwritten parallel code, the infeasibility of fine-tuning GrPPI is a crucial drawback for emerging applications. Despite this, programmability experiments estimate that GrPPI can potentially reduce the development time of parallel applications by about three times.

show abstract

Analyzing Programming Effort Model Accuracy of High-Level Parallel Programs for Stream Processing

Cited by 5 publications

References 15 publications

Performance and Programmability of GrPPI for Parallel Stream Processing on Multi-Cores

Performance and Programmability of GrPPI for Parallel Stream Processing on Multi-Cores

Assessing Application Efficiency and Performance Portability in Single-Source Programming for Heterogeneous Parallel Systems

Performance and programmability of GrPPI for parallel stream processing on multi-cores

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