Publication | Closed Access
Firepile
41
Citations
19
References
2011
Year
Unknown Venue
Hardware SecurityRecent AdvancesGpu ArchitectureEngineeringCompute KernelGpu BenchmarkingProgram AnalysisDynamic Memory AllocationComputer EngineeringComputer ArchitectureParallel ProgrammingComputer ScienceParallel ComputingGpu ClusterSystem SoftwareGpu MemoryGpu Computing
Recent advances have enabled GPUs to be used as general-purpose parallel processors on commodity hardware for little cost. However, the ability to program these devices has not kept up with their performance. The programming model for GPUs has a number of restrictions that make it difficult to program. For example, software running on the GPU cannot perform dynamic memory allocation, requiring the programmer to pre-allocate all memory the GPU might use. To achieve good performance, GPU programmers must also be aware of how data is moved between host and GPU memory and between the different levels of the GPU memory hierarchy.
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