GPU-Programming

所属分类:GPU/显卡
开发工具:Cuda
文件大小:61KB
下载次数:0
上传日期:2021-05-12 17:26:59
上 传 者sh-1993
说明:  这是我所有GPU编程项目的回购。UARK GPU编程2021春季CSCE 4643。
(This is a repo for all of my GPU Programming projects. UARK GPU Programming Spring 2021 CSCE 4643.)

文件列表:
p1-vector-add (0, 2021-05-13)
p1-vector-add\kernel.cu (728, 2021-05-13)
p1-vector-add\main.cu (4050, 2021-05-13)
p1-vector-add\p1_miller.tar (10240, 2021-05-13)
p2-sgemm (0, 2021-05-13)
p2-sgemm\Makefile (413, 2021-05-13)
p2-sgemm\answers.txt (516, 2021-05-13)
p2-sgemm\kernel.cu (2206, 2021-05-13)
p2-sgemm\main.cu (5119, 2021-05-13)
p2-sgemm\p2_miller.tar (17408, 2021-05-13)
p2-sgemm\support.cu (1336, 2021-05-13)
p2-sgemm\support.h (1008, 2021-05-13)
p3-sgemm-tiled (0, 2021-05-13)
p3-sgemm-tiled\Makefile (419, 2021-05-13)
p3-sgemm-tiled\kernel.cu (2893, 2021-05-13)
p3-sgemm-tiled\main.cu (5091, 2021-05-13)
p3-sgemm-tiled\p3_miller.tar (14336, 2021-05-13)
p3-sgemm-tiled\support.cu (1456, 2021-05-13)
p3-sgemm-tiled\support.h (1008, 2021-05-13)
p4-convolution (0, 2021-05-13)
p4-convolution\Makefile (419, 2021-05-13)
p4-convolution\answers.txt (2152, 2021-05-13)
p4-convolution\kernel.cu (1673, 2021-05-13)
p4-convolution\main.cu (4153, 2021-05-13)
p4-convolution\p4_miller.tar (17920, 2021-05-13)
p4-convolution\support.cu (3074, 2021-05-13)
p4-convolution\support.h (1450, 2021-05-13)
p5-reduction (0, 2021-05-13)
p5-reduction\Makefile (417, 2021-05-13)
p5-reduction\answers.txt (355, 2021-05-13)
p5-reduction\kernel.cu (1354, 2021-05-13)
p5-reduction\main.cu (4395, 2021-05-13)
p5-reduction\p5_miller.tar (8192, 2021-05-13)
p5-reduction\support.cu (1499, 2021-05-13)
p5-reduction\support.h (1026, 2021-05-13)
p6-prefix-scan (0, 2021-05-13)
p6-prefix-scan\Makefile (419, 2021-05-13)
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# GPU Programming This is a repo for all of my GPU Programming projects. UARK GPU Programming Spring 2021 CSCE 4***3. This course introduced massively parallel programming using Graphics Processing Units (GPUs). Basic programming model, GPU thread hierarchy, and GPU memory architecture were covered. Various performance optimization techniques and parallel patterns was discussed to deal with real-life applications.

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