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Electronics
Special Issues
Emerging Technologies of High-Performance and Parallel Computing
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Emerging Technologies of High-Performance and Parallel Computing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 4538

Special Issue Editor

Dr. Pedro Valero-Lara

E-Mail Website
Guest Editor
Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Interests: high performance computing; parallel programming; linear algebra; computational fluid dynamics

Special Issue Information

Dear Colleagues,

The end of the Moore’s law and the need for energy-efficiency solutions have brought new challenges to continue increasing performance. These new challenges, but also new opportunities, affect all levels of the computing stack. New architectures, such as heterogeneous cores, deep memory hierarchies, near memory processing, non-von Neuman architectures, among many others, have emerged as a possible solution to address such important challenges. In a more and more complex and specialized upcoming world in terms of computer design, the efforts for a new software stack (programing models, algorithms, libraries, runtime, etc.) is more important and necessary than ever. This Special Issue aims to promote any research or development targeting potential solutions to the challenges above presented at any level of the computing stack, being particularly important power-efficiency architectures and tools targeting performance portability and/or performance optimization. Papers are being sought on many aspects of emerging computing including (but not limited to):

  • Emerging solutions for High Performance Computing Applications and Problems;
  • Emerging Parallel Programming Paradigms for Performance Portability and/or Performance Optimization;
  • Reliability/Benchmarking/Measurements on Emerging Platforms;
  • Emerging Computer Architectures;
  • Standardization on Emerging Platforms;

Dr. Pedro Valero-Lara
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • emerging solutions for high peformance computing applications and problems
  • emerging parallel programming paradigms for performance portability and/or peformance optimization
  • reliability/benchmarking/measurements on emerging platforms
  • emerging computer architectures
  • standarization and porting efforts on emerging patforms

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Published Papers (2 papers)

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Research

18 pages, 626 KiB  
Article
Towards Enhancing Coding Productivity for GPU Programming Using Static Graphs
by Leonel Toledo, Pedro Valero-Lara, Jeffrey S. Vetter and Antonio J. Peña
Electronics 2022, 11(9), 1307; https://doi.org/10.3390/electronics11091307 - 20 Apr 2022
Cited by 1 | Viewed by 1715
Abstract
The main contribution of this work is to increase the coding productivity of GPU programming by using the concept of Static Graphs. GPU capabilities have been increasing significantly in terms of performance and memory capacity. However, there are still some problems in terms [...] Read more.
The main contribution of this work is to increase the coding productivity of GPU programming by using the concept of Static Graphs. GPU capabilities have been increasing significantly in terms of performance and memory capacity. However, there are still some problems in terms of scalability and limitations to the amount of work that a GPU can perform at a time. To minimize the overhead associated with the launch of GPU kernels, as well as to maximize the use of GPU capacity, we have combined the new CUDA Graph API with the CUDA programming model (including CUDA math libraries) and the OpenACC programming model. We use as test cases two different, well-known and widely used problems in HPC and AI: the Conjugate Gradient method and the Particle Swarm Optimization. In the first test case (Conjugate Gradient) we focus on the integration of Static Graphs with CUDA. In this case, we are able to significantly outperform the NVIDIA reference code, reaching an acceleration of up to 11× thanks to a better implementation, which can benefit from the new CUDA Graph capabilities. In the second test case (Particle Swarm Optimization), we complement the OpenACC functionality with the use of CUDA Graph, achieving again accelerations of up to one order of magnitude, with average speedups ranging from 2× to 4×, and performance very close to a reference and optimized CUDA code. Our main target is to achieve a higher coding productivity model for GPU programming by using Static Graphs, which provides, in a very transparent way, a better exploitation of the GPU capacity. The combination of using Static Graphs with two of the current most important GPU programming models (CUDA and OpenACC) is able to reduce considerably the execution time w.r.t. the use of CUDA and OpenACC only, achieving accelerations of up to more than one order of magnitude. Finally, we propose an interface to incorporate the concept of Static Graphs into the OpenACC Specifications. Full article
(This article belongs to the Special Issue Emerging Technologies of High-Performance and Parallel Computing)
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13 pages, 2412 KiB  
Article
ForkJoinPcc Algorithm for Computing the Pcc Matrix in Gene Co-Expression Networks
by Amel Ali Alhussan, Hussah Nasser AlEisa, Ghada Atteia, Nahed H. Solouma, Rania Ahmed Abdel Azeem Abul Seoud, Ola S. Ayoub, Vidan F. Ghoneim and Nagwan Abdel Samee
Electronics 2022, 11(8), 1174; https://doi.org/10.3390/electronics11081174 - 7 Apr 2022
Cited by 6 | Viewed by 1943
Abstract
High-throughput microarrays contain a huge number of genes. Determining the relationships between all these genes is a time-consuming computation. In this paper, the authors provide a parallel algorithm for finding the Pearson’s correlation coefficient between genes measured in the Affymetrix microarrays. The main [...] Read more.
High-throughput microarrays contain a huge number of genes. Determining the relationships between all these genes is a time-consuming computation. In this paper, the authors provide a parallel algorithm for finding the Pearson’s correlation coefficient between genes measured in the Affymetrix microarrays. The main idea in the proposed algorithm, ForkJoinPcc, mimics the well-known parallel programming model: the fork–join model. The parallel MATLAB APIs have been employed and evaluated on shared or distributed multiprocessing systems. Two performance metrics—the processing and communication times—have been used to assess the performance of the ForkJoinPcc. The experimental results reveal that the ForkJoinPcc algorithm achieves a substantial speedup on the cluster platform of 62× compared with a 3.8× speedup on the multicore platform. Full article
(This article belongs to the Special Issue Emerging Technologies of High-Performance and Parallel Computing)
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