The lack of training in parallel programming as well as support from independent software vendors are key obstacles hindering wider adoption of computation on graphics processing units in high-performance computing, an Nvidia executive has said.
Simon See, chief solution architect and director of solution architecture at Nvidia, noted that both research and production sectors use high-performance computing (HPC) for modelling and simulation but the two sectors face different challenges in the adoption of general-purpose GPU computing.
For researchers, the main challenge lies in the lack of training in parallel programming, said See, who was speaking to reporters at the GPU Technology Conference in Singapore on Thursday. While it is possible for researchers to write their own computational codes for HPCs, most are not able to "think in parallel programming", which prevents them from applying the technology, he said.
For more on this ZDNet UK-selected story, see Lack of training hinders GPU in HPC on ZDNet Asia.
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