A Dwarf-based Scalable Big Data Benchmarking Methodology

TL;DR

This paper introduces a scalable big data benchmarking methodology based on eight fundamental data dwarfs, enabling efficient and accurate proxy benchmarks that preserve key architectural characteristics across different hardware platforms.

Contribution

It identifies eight big data dwarfs and develops proxy benchmarks using DAG-like combinations, significantly reducing simulation time while maintaining high accuracy.

Findings

Proxy benchmarks shorten simulation time by 100x.

Micro-architecture accuracy exceeds 90%.

Method is applicable to various hardware architectures.

Abstract

Different from the traditional benchmarking methodology that creates a new benchmark or proxy for every possible workload, this paper presents a scalable big data benchmarking methodology. Among a wide variety of big data analytics workloads, we identify eight big data dwarfs, each of which captures the common requirements of each class of unit of computation while being reasonably divorced from individual implementations. We implement the eight dwarfs on different software stacks, e.g., OpenMP, MPI, Hadoop as the dwarf components. For the purpose of architecture simulation, we construct and tune big data proxy benchmarks using the directed acyclic graph (DAG)-like combinations of the dwarf components with different weights to mimic the benchmarks in BigDataBench. Our proxy benchmarks preserve the micro-architecture, memory, and I/O characteristics, and they shorten the simulation time by 100s times while maintain the average micro-architectural data accuracy above 90 percentage on both X86 64 and ARMv8 processors. We will open-source the big data dwarf components and proxy benchmarks soon.