Thesis etd-09252012-162940 |
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Thesis type
Tesi di laurea magistrale
URN
etd-09252012-162940
Thesis title
Data Parallel Patterns targeting CPU/GPU mix
Department
INFORMATICA
Course of study
INFORMATICA E NETWORKING
Supervisors
.
relatore Prof. Danelutto, Marco
controrelatore Prof. Coppola, Massimo
controrelatore Prof. Coppola, Massimo
Keywords
- cost models
- gpgpu
- map
- multi-core
- parallel patterns
- reduce
Graduation session start date
12/10/2012
Availability
Withheld
Release date
12/10/2052
Abstract (Inglese)
Abstract (Italiano)
The purpose of this thesis is to present some possible implementations and introduce cost models for evaluating the performance of data-parallel computations (map, reduce) that use both the CPU and GPU in order to maximize resource usage. We will then use these cost models to determine the best input data split between the two computational units that optimizes performance and study its precision on a few instances of the aforementioned data-parallel paradigms.
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