CUDA Benchmark

Purpose

The purpose of this benchmark is to prove that parallel computing on gpu does significantly improves program performance in terms of speed. Also, this benchmark gives an estimate of the performance increase.

Experiment 1

Environment

Alienware 14R2 (i5-6GB-GT650M)
Ubuntu14.04 CUDA7.5 GCC4.8.2

Code

C++11
CUDA

Time Elapsed:

C++11: 5418.464355 millsec
CUDA: 229.8250 millsec

Performance Evaluation

C++11: CUDA = 23.6
CUDA Time Percentage = 4%

Experiment 2

Environment

Alienware 14R2 (i5-6GB-GT650M)
Ubuntu14.04 CUDA7.5 GCC4.8.2
Tensorflow 0.9
Tensorflow 0.8

Code

Python2.7 with Tensorflow 0.9 CPU
Python3.5 with Tensorflow 0.8 GPU

Time Elapsed:

CPU: Time Elapsed: 298.434136 s
GPU: 144.865046 s

Performance Evaluation

CPU: CUDA = 2.06
GPU Time Percntage = 48.5%

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