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Support for Data Parallelism in the CAL Actor Language

Gebrewahid, Essayas; Arslan, Mehmet Ali LU ; Karlsson, Andreas and ul-Abdin, Zain (2016) WPMVP 2016 - 3rd Workshop on Programming Models for SIMD/Vector Processing In WPMVP 2016 - 3rd Workshop on Programming Models for SIMD/Vector Processing
Abstract
With the arrival of heterogeneous manycores comprising various features to support task, data and instruction-level parallelism, developing applications that take full advantage of the hardware parallel features has become a major challenge. In this paper, we present an extension to our CAL compilation framework (CAL2Many) that supports data parallelism in the CAL Actor Language. Our compilation framework makes it possible to pro- gram architectures with SIMD support using high-level language and provides efficient code generation. We support general SIMD instructions but the code generation backend is currently implemented for two custom architectures, namely ePUMA and EIT. Our experiments were carried out for two custom SIMD processor... (More)
With the arrival of heterogeneous manycores comprising various features to support task, data and instruction-level parallelism, developing applications that take full advantage of the hardware parallel features has become a major challenge. In this paper, we present an extension to our CAL compilation framework (CAL2Many) that supports data parallelism in the CAL Actor Language. Our compilation framework makes it possible to pro- gram architectures with SIMD support using high-level language and provides efficient code generation. We support general SIMD instructions but the code generation backend is currently implemented for two custom architectures, namely ePUMA and EIT. Our experiments were carried out for two custom SIMD processor architectures using two applications. The experiment shows the possibility of achieving performance comparable to hand-written machine code with much less programming effort. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
SIMD, CAL Actor Language, QRD
in
WPMVP 2016 - 3rd Workshop on Programming Models for SIMD/Vector Processing
publisher
ACM
conference name
WPMVP 2016 - 3rd Workshop on Programming Models for SIMD/Vector Processing
external identifiers
  • Scopus:84976580876
ISBN
978-1-4503-4060-1
DOI
10.1145/2870650.2870656
language
English
LU publication?
yes
id
4f9b87db-ed4d-4460-a656-aabe378599f6
date added to LUP
2016-05-13 15:54:54
date last changed
2016-10-13 05:08:35
@misc{4f9b87db-ed4d-4460-a656-aabe378599f6,
  abstract     = {With the arrival of heterogeneous manycores comprising various features to support task, data and instruction-level parallelism, developing applications that take full advantage of the hardware parallel features has become a major challenge. In this paper, we present an extension to our CAL compilation framework (CAL2Many) that supports data parallelism in the CAL Actor Language. Our compilation framework makes it possible to pro- gram architectures with SIMD support using high-level language and provides efficient code generation. We support general SIMD instructions but the code generation backend is currently implemented for two custom architectures, namely ePUMA and EIT. Our experiments were carried out for two custom SIMD processor architectures using two applications. The experiment shows the possibility of achieving performance comparable to hand-written machine code with much less programming effort.},
  author       = {Gebrewahid, Essayas and Arslan, Mehmet Ali and Karlsson, Andreas and ul-Abdin, Zain},
  isbn         = {978-1-4503-4060-1},
  keyword      = {SIMD,CAL Actor Language,QRD},
  language     = {eng},
  publisher    = {ARRAY(0xa0c7aa8)},
  series       = {WPMVP 2016 - 3rd Workshop on Programming Models for SIMD/Vector Processing},
  title        = {Support for Data Parallelism in the CAL Actor Language},
  url          = {http://dx.doi.org/10.1145/2870650.2870656 },
  year         = {2016},
}