Parallelization of large vector similarity computations in a hybrid CPU+GPU environment

The paper presents design, implementation and tuning of a hybrid parallel OpenMP+CUDA code for computation of similarity between pairs of a large number of multidimensional vectors. The problem has a wide range of applications, and consequently its optimization is of high importance, especially on c...

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Vydané v:The Journal of supercomputing Ročník 74; číslo 2; s. 768 - 786
Hlavný autor: Czarnul, Paweł
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.02.2018
Springer Nature B.V
Predmet:
Central processing units
Coalescing
Compilers
Computation
Computer Science
CPUs
Hybrid systems
Interpreters
Memory management
Optimization
Parallel processing
Processor Architectures
Programming Languages
Similarity
Tuning
CUDA
Parallel programming
Hybrid parallelism
OpenMP
Optimization
ISSN:0920-8542, 1573-0484
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Shrnutí:The paper presents design, implementation and tuning of a hybrid parallel OpenMP+CUDA code for computation of similarity between pairs of a large number of multidimensional vectors. The problem has a wide range of applications, and consequently its optimization is of high importance, especially on currently widespread hybrid CPU+GPU systems targeted in the paper. The following are presented and tested for computation of all vector pairs: tuning of a GPU kernel with consideration of memory coalescing and using shared memory, minimization of GPU memory allocation costs, optimization of CPU–GPU communication in terms of size of data sent, overlapping CPU–GPU communication and kernel execution, concurrent kernel execution, determination of best sizes for data batches processed on CPUs and GPUs along with best GPU grid sizes. It is shown that all codes scale in hybrid environments with various relative performances of compute devices, even for a case when comparisons of various vector pairs take various amounts of time. Tests were performed on two high-performance hybrid systems with: 2 x Intel Xeon E5-2640 CPU + 2 x NVIDIA Tesla K20m and latest generation 2 x Intel Xeon CPU E5-2620 v4 + NVIDIA’s Pascal generation GTX 1070 cards. Results demonstrate expected improvements and beneficial optimizations important for users incorporating such types of computations into their parallel codes run on similar systems.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-017-2159-7