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Efficient Exception Handling Support for GPUs

机译:高效处理GPU的异常处理支持

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Operating systems have long relied on the exception handling mechanism to implement numerous virtual memory features and optimizations. However, today's GPUs have a limited support for exceptions, which prevents implementation of such techniques. The existing solution forwards GPU memory faults to the CPU while the faulting instruction is stalled in the GPU pipeline. This approach prevents preemption of the faulting threads, and results in underutilized hardware resources while the page fault is being resolved by the CPU. In this paper, we present three schemes for supporting GPU exceptions that allow the system software to preempt and restart the execution of the faulting code. There is a trade-off between the performance overhead introduced by adding exception support and the additional complexity. Our solutions range from 90% of the baseline performance with no area overheads, to 99.2% of the baseline performance with less than 1% area and 2% power overheads. Experimental results also show 10% performance improvement on some benchmarks when using this support to context switch the GPU during page migrations, to hide their latency. We further observe up to 1.75x average speedup when implementing lazy memory allocation on the GPU, also possible thanks to our exception handling support.
机译:操作系统长期依赖于异常处理机制来实现众多虚拟内存功能和优化。但是,今天的GPU对例外的支持有限,这可以防止实施此类技术。现有解决方案在GPU管道中停止故障指令时将GPU存储器故障转发给CPU。此方法可防止抢占故障线程,并且在CPU解析页面错误时导致未充分利用的硬件资源。在本文中,我们提出了三个方案,用于支持GPU异常,允许系统软件抢占并重新启动故障代码的执行。通过添加异常支持和额外复杂性引入的性能开销之间存在权衡。我们的解决方案的范围从基线性能的90%,没有区域开销,占基线性能的99.2%,面积小于1%和2%的电源开销。实验结果还显示了在页面迁移期间使用此支持的上下文切换GPU时,对某些基准进行了10%的性能改进,以隐藏其延迟。由于我们的异常处理支持,我们进一步观察到GPU上的懒惰内存分配时高达1.75倍的平均速度。

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