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The execution time variation of tasks is efficiently captured by a memory access workload model.
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This paper revisits the IVM-based B&B algorithm on the GPU, addressing the irregularity of the algorithm in terms of workload, memory access patterns and control flow.
By carefully considering memory access locality when dividing the workload among blocks of threads, the GPU's cache is used more efficiently, making more effective use of the available memory bandwidth.
By using GPGPU-SODA, we observe that several microarchitecture structures in GPGPUs exhibit high soft-error susceptibility, and the structure vulnerability is sensitive to the workload characteristics (e.g. branch divergences, memory access pattern).
An artificial workload was chosen instead of a real application because the memory access patterns of real processes are very variable, depending on their purpose and even in what they are doing at a given moment.
Nonetheless, this workload may be considered representative of applications having large working sets and random memory access patterns, similar to the memory-intensive QSim and SMV applications studied in [36] and the HashCache application used in [1].
Random direct memory access.
This reduces memory access.
(a) Using Global memory access.
Finally, a "Memory-Ctrl" module manages the intermediate memory access.
Recursive Gaussian filters prefer temporary memory access.
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