BEGIN:VCALENDAR
PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20121114T001500Z
DTEND:20121114T020000Z
LOCATION:East Entrance
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: GPU usage greatly decreases the time needed to optimize the route, however requires a complicated and well tuned implementation. With the increasing problem size, the time spent on comparing the graph edges grows significantly. We used instances from the TSPLIB library for for testing and our results show that by using our GPU algorithm, the time needed to perform a simple local search operation can be decreased approximately 5 to 45 times compared to parallel CPU code implementation using 6 cores. The experimental studies have shown that the optimization algorithm using the GPU local search converges from up to 300 times faster on average compared to the sequential CPU version, depending on the problem size. The main contributions of this work are the problem division scheme exploiting data locality which allows to solve arbitrarily big problem instances using GPU and the parallel implementation of the algorithm itself.
SUMMARY:High Performance GPU Accelerated TSP Solver
PRIORITY:3
END:VEVENT
END:VCALENDAR
BEGIN:VCALENDAR
PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20121114T001500Z
DTEND:20121114T020000Z
LOCATION:East Entrance
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: GPU usage greatly decreases the time needed to optimize the route, however requires a complicated and well tuned implementation. With the increasing problem size, the time spent on comparing the graph edges grows significantly. We used instances from the TSPLIB library for for testing and our results show that by using our GPU algorithm, the time needed to perform a simple local search operation can be decreased approximately 5 to 45 times compared to parallel CPU code implementation using 6 cores. The experimental studies have shown that the optimization algorithm using the GPU local search converges from up to 300 times faster on average compared to the sequential CPU version, depending on the problem size. The main contributions of this work are the problem division scheme exploiting data locality which allows to solve arbitrarily big problem instances using GPU and the parallel implementation of the algorithm itself.
SUMMARY:High Performance GPU Accelerated TSP Solver
PRIORITY:3
END:VEVENT
END:VCALENDAR