The GAP Benchmark Suite

By Scott Beamer, Krste Asanovic, David A. Patterson. arXiv.org, 2015

638 citations recorded by the source corpus.

We present a graph processing benchmark suite with the goal of helping to standardize graph processing evaluations. Fewer differences between graph processing evaluations will make it easier to compare different research efforts and quantify improvements. The benchmark not only specifies graph kernels, input graphs, and evaluation methodologies, but it also provides optimized baseline implementations. These baseline implementations are representative of state-of-the-art performance, and thus new contributions should outperform them to demonstrate an improvement. The input graphs are sized appropriately for shared memory platforms, but any implementation on any platform that conforms to the benchmark's specifications could be compared. This benchmark suite can be used in a variety of settings. Graph framework developers can demonstrate the generality of their programming model by implementing all of the benchmark's kernels and delivering competitive performance on all of the benchmark's graphs. Algorithm designers can use the input graphs and the baseline implementations to demonstrate their contribution. Platform designers and performance analysts can use the suite as a workload representative of graph processing.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/ae4a53795799dccf13fb773b644affeefd400856

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tushar Krishna
2. Srini Devadas
3. Saman P. Amarasinghe
4. Joel S. Emer
5. Vladimir Kiriansky
6. Dibakar Gope
7. Hanhwi Jang
8. Ilia A. Lebedev

In Computer Vision and Pattern Recognition

1. Tushar Krishna
2. Joel S. Emer
3. Jason Lowe-Power
4. Srini Devadas
5. Dibakar Gope
6. Vivienne Sze
7. Yu-hsin Chen
8. Tien-Ju Yang

In Information Systems

1. Tushar Krishna
2. Srini Devadas
3. Joel S. Emer

Citations in the corpus

• Pond: CXL-Based Memory Pooling Systems for Cloud Platforms (2022, neutral): “The experiment evaluates four internal workloads: an audio/video conferencing application, a database service, a key-value store, and a business analytics service.”
• DAWG: A Defense Against Cache Timing Attacks in Speculative Execution Processors (2018, methodology): “9 shows the performance of protection domains using different fractions of an L3 cache for 4-thread instances of graph applications from GAPBS.”
• The gem5 Simulator: Version 20.0+ (2020, methodology): “As part of gem5-20, we have tested the simulator’s capability to run SPEC 2006 [35], SPEC 2017 [21], PARSEC [18], the NAS Parallel Benchmarks (NPB) [15], and the GAP Benchmark Suite (GAPBS) [16].”