Reconciling abstraction with high performance: A metaocaml approach

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

A common application of generative programming is building highperformance computational kernels highly tuned to the problem at hand. A typical linear algebra kernel is specialized to the numerical domain (rational, float, double, etc.), loop unrolling factors, array layout and a priori knowledge (e.g., the matrix being positive definite). It is tedious and error prone to specialize by hand, writing numerous variations of the same algorithm. The widely used generators such as ATLAS and SPIRAL reliably produce highly tuned specialized code but are difficult to extend. In ATLAS, which generates code using printf, even balancing parentheses is a challenge. According to the ATLAS creator, debugging is nightmare. A typed staged programming language such as MetaOCaml lets us state a general, obviously correct algorithm and add layers of specializations in a modular way. By ensuring that the generated code always compiles and letting us quickly test it, MetaOCaml makes writing generators less daunting and more productive. The readers will see it for themselves in this hands-on tutorial. Assuming no prior knowledge of MetaOCaml and only a basic familiarity with functional programming, we will eventually implement a simple domain-specific language (DSL) for linear algebra, with layers of optimizations for sparsity and memory layout of matrices and vectors, and their algebraic properties. We will generate optimal BLAS kernels. We shall get the taste of the "Abstraction without guilt".

Original languageEnglish
Pages (from-to)1-101
Number of pages101
JournalFoundations and Trends in Programming Languages
Volume5
Issue number1
DOIs
Publication statusPublished - 2018

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computer Science Applications

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