GPU acceleration is the concept of accelerating the execution speed of an application by running it on the GPU. Researchers and developers have always wanted to achieve greater speed for their applications and GPU acceleration is a very common way of doing so. This has been done a long time for highly graphical applications using powerful dedicated GPUs. However, researchers have become more and more interested in using GPU acceleration on everyday applications. Moreover now a days more or less every computer has some sort of integrated GPU which often is underutilized. The integrated GPUs are not as powerful as dedicated ones but they have other benefits such as a lower power consumption and faster data transfer. Therefore this thesis’ purpose was to examine whether the integrated GPU Intel HD 4600 can be used to accelerate the two applications Image Convolution and sparse matrix vector multiplication (SpMV). This was done by analysing the code from a previous thesis which produced some unexpected results as well as a benchmark from the OpenDwarf’s benchmark suite. The Intel HD 4600 was able to speedup both Image Convolution and SpMV by about two times compared to running them on the Intel i7-4790. However, the SpMV implementation was not well suited for the GPU meaning that the speedup was only observed on ideal input configurations.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-140124 |
| Date | January 2017 |
| Creators | Arvid, Johnsson |
| Publisher | Linköpings universitet, Programvara och system |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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