C
Carter McCardwell
Researcher at Northeastern University
Publications - 5
Citations - 157
Carter McCardwell is an academic researcher from Northeastern University. The author has contributed to research in topics: Microarchitecture & Benchmark (computing). The author has an hindex of 3, co-authored 5 publications receiving 117 citations.
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Proceedings ArticleDOI
Hetero-mark, a benchmark suite for CPU-GPU collaborative computing
Yifan Sun,Xiang Gong,Amir Kavyan Ziabari,Leiming Yu,Xiangyu Li,Saoni Mukherjee,Carter McCardwell,Alejandro Villegas,David Kaeli +8 more
TL;DR: The Hetero-Mark is proposed to help heterogeneous system programmers understand CPU-GPU collaborative computing and to provide guidance to computer architects in order to enhance the design of the runtime and the driver.
Proceedings ArticleDOI
MGPUSim: enabling multi-GPU performance modeling and optimization
Yifan Sun,Trinayan Baruah,Saiful A. Mojumder,Shi Dong,Xiang Gong,Shane Treadway,Yuhui Bao,Spencer Hance,Carter McCardwell,Vincent Zhao,Harrison Barclay,Amir Kavyan Ziabari,Zhongliang Chen,Rafael Ubal,José L. Abellán,John Kim,Ajay Joshi,David Kaeli +17 more
TL;DR: This work presents MGPUSim, a cycle-accurate, extensively validated, multi-GPU simulator, based on AMD's Graphics Core Next 3 (GCN3) instruction set architecture, and proposes the Locality API, an API extension that allows the GPU programmer to both avoid the complexity of multi- GPU programming, while precisely controlling data placement in the multi- GPUs memory.
Proceedings ArticleDOI
Exploring the features of OpenCL 2.0
Saoni Mukherjee,Xiang Gong,Leiming Yu,Carter McCardwell,Yash Ukidave,Tuan Dao,Fanny Nina Paravecino,David Kaeli +7 more
TL;DR: This work introduces the latest runtime features enabled in OpenCL 2.0, and discusses how well the sample applications can benefit from some of these features.
FIR filtering and AES encryption with OpenCL 2.0
TL;DR: The latest parallel programming features supported in the OpenCL 2.0 standard are evaluated using shared virtual memory and dynamic parallelism to accelerate two example applications.
Journal ArticleDOI
Student cluster competition 2017, team Northeastern University: Reproducing vectorization of the Tersoff multi-body potential on the NVIDIA V100
Z. Marcus,J. Booth,Christopher C. Bunn,M. Leger,Spencer Hance,T. Sweeney,Carter McCardwell,David Kaeli +7 more
TL;DR: This paper evaluates the reproducibility of an efficient and portable Tersoff potential calculation using LAMMPS, previously presented at the Supercomputing ’16 Conference, in terms of portability and performance.