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For over two decades the dominant means for enabling portable performance of computational science and engineering applications on parallel processing architectures has been the bulk-synchronous parallel programming model. Code developers, motivated by performance considerations to minimize the number of messages transmitted, have typically strived to increase the size of each message through aggregation...
Briareus provides convenient tools to make use of computing resources provided by cloud to accelerate Python applications. In this paper, three techniques are presented. First, some of the functions in a Python program can be migrated to cloud and be evaluated using the hardware and software provided by that cloud platform, while the other parts still running locally. Second, Briareus can automatically...
This paper presents a high performance GPU accelerated implementation of 2-opt local search algorithm for the Traveling Salesman Problem (TSP). GPU usage significantly decreases the execution time needed for tour optimization, however it also requires a complicated and well tuned implementation. With the problem size growing, the time spent on local optimization comparing the graph edges grows significantly...
With many-core processor architectures emerging, concerns arise regarding the productivity of numerous parallel programming tools, models, and languages as developers from a broad spectrum of science domains struggle to maximize performance and maintain correctness of their applications. Fortunately, a partitioned global address space (PGAS) programming model has demonstrated realizable performance...
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