Title
Offloading strategies for Stencil kernels on the KNC Xeon Phi architecture: Accuracy versus performance
Abstract
AbstractThe ever-increasing computational requirements of HPC and service provider applications are becoming a great challenge for hardware and software designers. These requirements are reaching levels where the isolated development on either computational field is not enough to deal with such challenge. A holistic view of the computational thinking is therefore the only way to success in real scenarios. However, this is not a trivial task as it requires, among others, of hardware–software codesign. In the hardware side, most high-throughput computers are designed aiming for heterogeneity, where accelerators (e.g. Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), etc.) are connected through high-bandwidth bus, such as PCI-Express, to the host CPUs. Applications, either via programmers, compilers, or runtime, should orchestrate data movement, synchronization, and so on among devices with different compute and memory capabilities. This increases the programming complexity and it may reduce the overall application performance. This article evaluates different offloading strategies to leverage heterogeneous systems, based on several cards with the first-generation Xeon Phi coprocessors (Knights Corner). We use a 11-point 3-D Stencil kernel that models heat dissipation as a case study. Our results reveal substantial performance improvements when using several accelerator cards. Additionally, we show that computing of an approximate result by reducing the communication overhead can yield 23% performance gains for double-precision data sets.
Year
DOI
Venue
2020
10.1177/1094342017738352
Periodicals
Keywords
Field
DocType
Offloading computation, Stencil codes, approximate computing, heterogeneous computing
Computer architecture,Computer science,Xeon Phi,Stencil,Parallel computing,Symmetric multiprocessor system,Stencil code,Field-programmable gate array,Compiler,Software,Programming complexity
Journal
Volume
Issue
ISSN
34
2
1094-3420
Citations 
PageRank 
References 
0
0.34
4
Authors
4
Name
Order
Citations
PageRank
Mario Hernández110.69
Juan Manuel Cebrian22410.19
José M. Cecilia316622.28
jose m garcia410513.66