Speeding Up Compressed Sensing Algorithms

ScottCase Studies, CUDA 1 Comment

Are you looking for ways to speed up compressed sensing? If you work in the areas of medical image reconstruction, image acquisition or sensor networks, you probably are. This paper, Parallel Implementation of Compressed Sensing Algorithm on CUDA-GPU, compares CPUs running Matlab and GPUs running Jacket using a Basis Pursuit Algorithm for compressed sensing.

They compared an Intel Core 2 Duo T8100 (2.1GHz and 3.0 GB memory) running Matlab with a NVIDIA GeForce series 8400m GS (256 MB video memory, DDR2 and bus width of 64bit) using an older version of Jacket, Version 1.3.

The CPU and GPU setups were used to run their Basis Pursuit Algorithm on six MRI images. These are some samples:


The implementation using Jacket and GPUs saw speed-ups of 8x. Here is the comparison graph:


By utilizing Jacket and GPUs, these guys reduced compressed sensing execution time from 50 seconds to 6 seconds for several bio-medical images.

If you work with compressed sensing, you can speed up your implementations. When you start using Jacket and GPUs, you’ll start wondering what to do with all your newfound free time.

We want to thank Kuldeep Yadav, Ankush Mittal, M.A. Ansar and Avi Srivastava for all their hard work putting this study together. We appreciate you including Jacket in the study and look forward to seeing more great work from you in the future.

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