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Оценка локальности параллельных алгоритмов, реализуемых на графических процессорах

Оценка локальности параллельных алгоритмов, реализуемых на графических процессорах

Abstract

Исследуется задача получения блоков операций и потоков операций параллельного алгоритма, приводящих к меньшему числу обращений к глобальной памяти и к эффективному использованию параллельными потоками вычислений кэшей и разделяемой памяти графического процессора. Сформулированы и доказаны утверждения, позволяющие оценить объем коммуникационных операций, порождаемых альтернативными вариантами задания размеров блоков вычислений, а также минимизировать число промахов кэша за счет использования временной и пространственной локальности данных с учетом размера и длины строк кэша. Исследования конструктивны и допускают программную реализацию для практического использования.

The problem of obtaining blocks of operations and threads of parallel algorithm resulting in a smaller number of accesses to global memory and resulting in the efficient use of caches and shared memory graphics processor is investigated. We formulated and proved statements to assess the volume of communication transactions generated by alternative sizing of blocks, as well as to minimize the number of cache misses due to the use of temporal and spatial locality of data. The research is constructive and allows software implementation for practical use.

Keywords

ПАРАЛЛЕЛЬНЫЕ ВЫЧИСЛЕНИЯ,ГРАФИЧЕСКИЙ ПРОЦЕССОР,МИНИМИЗАЦИЯ ОБЪЕМА КОММУНИКАЦИОННЫХ ОПЕРАЦИЙ,ВРЕМЕННАЯ ЛОКАЛЬНОСТЬ,ПРОСТРАНСТВЕННАЯ ЛОКАЛЬНОСТЬ,PARALLEL COMPUTING,GPU,MINIMIZATION OF COMMUNICATIONS,TEMPORAL LOCALITY,SPATIAL LOCALITY

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold