Automatic SIMD vectorization for Haskell

PetersenLeaf,; OrchardDominic,; GlewNeal,

doi:10.1145/2544174.2500605

Cited by 5 publications

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Pure Functions in C: A Small Keyword for Automatic Parallelization

Süß

Nagel

Vef

et al. 2020

Int J Parallel Prog

View full text Add to dashboard Cite

The need for parallel task execution has been steadily growing in recent years since manufacturers mainly improve processor performance by increasing the number of installed cores instead of scaling the processor's frequency. To make use of this potential, an essential technique to increase the parallelism of a program is to parallelize loops. Several automatic loop nest parallelizers have been developed in the past such as PluTo. The main restriction of these tools is that the loops must be statically analyzable which, among other things, disallows function calls within the loops. In this article, we present a seemingly simple extension to the C programming language which marks functions without side-effects. These functions can then basically be ignored when the automatic parallelizer checks the parallelizability of loops. We integrated the approach into the GCC compiler toolchain and evaluated it by running several real-world applications. Our experiments show that the C extension helps to identify additional parallelization opportunities and, thus, to significantly increase the performance of applications.

show abstract

Pure Functions in C: A Small Keyword for Automatic Parallelization

Süß

Nagel

Vef

et al. 2020

Int J Parallel Prog

View full text Add to dashboard Cite

show abstract

Pure Functions in C: A Small Keyword for Automatic Parallelization

SuB

Nagel

Vef

et al. 2017

2017 IEEE International Conference on Cluster Computing (CLUSTER)

View full text Add to dashboard Cite

And Now for Something Completely Different: Running Lisp on GPUs

SuB

Döring

Brinkmann

et al. 2018

2018 IEEE International Conference on Cluster Computing (CLUSTER)

View full text Add to dashboard Cite

The internal parallelism of compute resources increases permanently, and graphics processing units (GPUs) and other accelerators have been gaining importance in many domains. Researchers from life science, bioinformatics or artificial intelligence, for example, use GPUs to accelerate their computations. However, languages typically used in some of these disciplines often do not benefit from the technical developments because they cannot be executed natively on GPUs. Instead existing programs must be rewritten in other, less dynamic programming languages. On the other hand, the gap in programming features between accelerators and common CPUs shrinks permanently. Since accelerators are becoming more competitive with regard to general computations, they will not be mere special-purpose processors in the future. It is a valid assumption that future GPU generations can be used in a similar or even the same way as CPUs and that compilers or interpreters will be needed for a wider range of computer languages.We present CuLi, an interactive Lisp interpreter, that performs all computations on a CUDA-capable GPU. The host system is needed only for the input and the output. At the moment, Lisp programs running on CPUs outperform Lisp programs on GPUs, but we present trends indicating that this might change in the future. Our study gives an outlook on the possibility of running Lisp programs or other dynamic programming languages on next-generation accelerators.

show abstract

Automatic SIMD vectorization for Haskell

Cited by 5 publications

References 17 publications

Pure Functions in C: A Small Keyword for Automatic Parallelization

Pure Functions in C: A Small Keyword for Automatic Parallelization

Pure Functions in C: A Small Keyword for Automatic Parallelization

And Now for Something Completely Different: Running Lisp on GPUs

Contact Info

Product

Resources

About