# The GAP Benchmark Suite

arXiv: https://arxiv.org/abs/1508.03619 · HTML: https://mlsanity.com/paper/1508.03619/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2015.

## Built on by (most cited first, 14 of 14)

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- [PaCKD: Pattern-Clustered Knowledge Distillation for Compressing Memory Access Prediction Models](https://mlsanity.com/paper/2402.13441/) (2024)
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- [Learning to Rank Graph-based Application Objects on Heterogeneous Memories](https://mlsanity.com/paper/2211.02195/) (2022)
- [Accelerating Computer Architecture Simulation through Machine Learning](https://mlsanity.com/paper/2402.18746/) (2024)
- [A Two Level Neural Approach Combining Off-Chip Prediction with Adaptive Prefetch Filtering](https://mlsanity.com/paper/2403.15181/) (2024)
- [Learning Semantics, Not Addresses: Runtime Neural Prefetching for Far Memory](https://mlsanity.com/paper/2506.00384/) (2025)
- [A Comparative Study of OpenMP Scheduling Algorithm Selection Strategies](https://mlsanity.com/paper/2507.20312/) (2025)

## Abstract (arXiv)

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.