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Programming hybrid CPU-GPU clusters is hard. This paper addresses this difficulty and presents the design and runtime implementation of <bold/><bold>Unicorn</bold><bold/>—a parallel programming model for hybrid CPU-GPU clusters. In particular, this paper proves that efficient distributed shared memory style programing is possible and its simplicity can be retained across CPUs...
We present a runtime system for simple and efficient programming of CPU+GPU clusters. The programmer focuses on core logic, while the system undertakes task allocation, load balancing, scheduling, data transfer, etc. Our programming model is based on a shared global address space, made efficient by transaction style bulk-synchronous semantics. This model broadly targets coarse-grained data parallel...
Work stealing is an effective load balancing technique in shared memory parallel programming. However, in a distributed setup researchers have pointed out difficulties in termination detection and in sustaining a healthy steal success rate. Keeping unsuccessful steal attempts to a minimum is especially important with many-core accelerators (having specialized engines for data copy-in and copy-out),...
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