A Study of Geodesic Distance Kernel on an Integrated GPU
- Argonne National Lab. (ANL), Argonne, IL (United States)
Graphics processing units (GPUs) are commonly used for graphics and general-purpose (GPGPU) computation. Currently, NVIDIA’s GPUs, through its CUDA framework for GPGPU programming, are ubiquitous for highperformance computing. Another class of GPUs, with a central processing unit (CPU) and a GPU integrated on the same chip, is commonly used in laptops, desktop computers, and low-power servers. While they are not designed to outperform discrete GPUs due to the power, area, and thermal constrains [1], there is a need to better understand the performance of a processor with an integrated GPU for floating-point intensive applications. The study helps us better understand the characteristics of hardware and software development tools, and the benefit of offloading such applications to an integrated GPU.
- Research Organization:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
- DOE Contract Number:
- AC02-06CH11357
- OSTI ID:
- 1576565
- Report Number(s):
- ANL/ALCF--19/3; 157463
- Country of Publication:
- United States
- Language:
- English
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