Forma: A framework for safe automatic array reshaping

  • Authors:

  • Peng Zhao;Shimin Cui;Yaoqing Gao;Raúl Silvera;José Nelson Amaral

  • Affiliations:

  • University of Alberta, Edmonton, AB, Canada;IBM Toronto Software Laboratory, ON, Canada;IBM Toronto Software Laboratory, ON, Canada;IBM Toronto Software Laboratory, ON, Canada;University of Alberta, Edmonton, AB, Canada

  • Venue:
  • ACM Transactions on Programming Languages and Systems (TOPLAS)
  • Year:
  • 2007

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Abstract

This article presents Forma, a practical, safe, and automatic data reshaping framework that reorganizes arrays to improve data locality. Forma splits large aggregated data-types into smaller ones to improve data locality. Arrays of these large data types are then replaced by multiple arrays of the smaller types. These new arrays form natural data streams that have smaller memory footprints, better locality, and are more suitable for hardware stream prefetching. Forma consists of a field-sensitive alias analyzer, a data type checker, a portable structure reshaping planner, and an array reshaper. An extensive experimental study compares different data reshaping strategies in two dimensions: (1) how the data structure is split into smaller ones (maximal partition × frequency-based partition × affinity-based partition); and (2) how partitioned arrays are linked to preserve program semantics (address arithmetic-based reshaping × pointer-based reshaping). This study exposes important characteristics of array reshaping. First, a practical data reshaper needs not only an inter-procedural analysis but also a data-type checker to make sure that array reshaping is safe. Second, the performance improvement due to array reshaping can be dramatic: standard benchmarks can run up to 2.1 times faster after array reshaping. Array reshaping may also result in some performance degradation for certain benchmarks. An extensive micro-architecture-level performance study identifies the causes for this degradation. Third, the seemingly naive maximal partition achieves best or close-to-best performance in the benchmarks studied. This article presents an analysis that explains this surprising result. Finally, address-arithmetic-based reshaping always performs better than its pointer-based counterpart.