# BigDataBench: a Big Data Benchmark Suite from Internet Services

arXiv: https://arxiv.org/abs/1401.1406 · HTML: https://mlsanity.com/paper/1401.1406/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2014.

## Built on by (most cited first, 8 of 8)

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- [GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra](https://mlsanity.com/paper/2103.03653/) (2021)
- [Stress Testing Chain-of-Thought Prompting for Large Language Models](https://mlsanity.com/paper/2309.16621/) (2023)
- [AutoStreamPipe: LLM Assisted Automatic Generation of Data Stream Processing Pipelines](https://mlsanity.com/paper/2510.23408/) (2025)
- [Does AI for science need another ImageNet Or totally different benchmarks? A case study of machine learning force fields](https://mlsanity.com/paper/2308.05999/) (2023)
- [Benchmarking Apache Spark and Hadoop MapReduce on Big Data Classification](https://mlsanity.com/paper/2209.10637/) (2022)
- [Enriching the Machine Learning Workloads in BigBench](https://mlsanity.com/paper/2406.10843/) (2024)
- [Machine Learning-Driven Intelligent Memory System Design: From On-Chip Caches to Storage](https://mlsanity.com/paper/2603.14583/) (2026)

## Abstract (arXiv)

As architecture, systems, and data management communities pay greater attention to innovative big data systems and architectures, the pressure of benchmarking and evaluating these systems rises. Considering the broad use of big data systems, big data benchmarks must include diversity of data and workloads. Most of the state-of-the-art big data benchmarking efforts target evaluating specific types of applications or system software stacks, and hence they are not qualified for serving the purposes mentioned above. This paper presents our joint research efforts on this issue with several industrial partners. Our big data benchmark suite BigDataBench not only covers broad application scenarios, but also includes diverse and representative data sets. BigDataBench is publicly available from http://prof.ict.ac.cn/BigDataBench . Also, we comprehensively characterize 19 big data workloads included in BigDataBench with varying data inputs. On a typical state-of-practice processor, Intel Xeon E5645, we have the following observations: First, in comparison with the traditional benchmarks: including PARSEC, HPCC, and SPECCPU, big data applications have very low operation intensity; Second, the volume of data input has non-negligible impact on micro-architecture characteristics, which may impose challenges for simulation-based big data architecture research; Last but not least, corroborating the observations in CloudSuite and DCBench (which use smaller data inputs), we find that the numbers of L1 instruction cache misses per 1000 instructions of the big data applications are higher than in the traditional benchmarks; also, we find that L3 caches are effective for the big data applications, corroborating the observation in DCBench.