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ACM SIGBED Review
In this paper, we present two conceptual frameworks for GPU applications to adjust their task execution times based on total workload. These frameworks enable smart GPU resource management when many applications share GPU resources while the workloads of those applications vary. Application developers can explicitly adjust the number of GPU cores depending on their needs. An implicit adjustment will be supported by a run-time framwork, which dynamically allocates the number of cores to tasksdoi:10.1145/2492385.2492387 fatcat:xhdgy6cohff4tehykp3xbfcfra