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Speeding Up Cycle Based Logic Simulation Using Graphics Processing Units

机译:使用图形处理单元加快基于周期的逻辑仿真

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Verification has grown to dominate the cost of electronic system design, consuming about 60% of design effort. Among several verification techniques, logic simulation remains the major verification technique. Speeding up logic simulation results in great savings and shorter time-to-market. We parallelize logic simulation using Graphics Processing Units (GPUs). In the past, GPUs were special-purpose application accelerators, suitable only for conventional graphics applications. The new generations of GPU architecture provide easier programmability and increased generality while maintaining the tremendous memory bandwidth and computational power of traditional GPUs. We develop a parallel cycle-based logic simulation algorithm that uses And Inverter Graphs (AIGs) as design representations. AIGs have proven to be an effective representation for various design automation applications, and we obtain similar benefits for speeding up logic simulation. We develop two clustering algorithms that partition the gates in the designs into independent blocks. Our algorithms exploit the massively parallel GPU architecture featuring thousands of concurrent threads, fast memory, and memory coalescing for optimizations. We demonstrate up-to 5x and 21x speedups on several benchmarks using our simulation system with the first and second clustering algorithms, respectively. Our work ultimately results in significant reduction in the overall design cycle.
机译:验证已逐渐占据电子系统设计成本的主导地位,消耗了约60%的设计工作量。在几种验证技术中,逻辑仿真仍然是主要的验证技术。加速逻辑仿真可节省大量成本,并缩短产品上市时间。我们使用图形处理单元(GPU)并行化逻辑仿真。过去,GPU是专用的应用程序加速器,仅适用于常规图形应用程序。新一代的GPU架构提供了更容易的可编程性和更高的通用性,同时保持了传统GPU的巨大内存带宽和计算能力。我们开发了一种基于并行循环的逻辑仿真算法,该算法使用“与逆变器图”(AIG)作为设计表示。 AIG已被证明是各种设计自动化应用的有效代表,并且我们在加速逻辑仿真方面也获得了类似的好处。我们开发了两种聚类算法,将设计中的门划分为独立的模块。我们的算法利用大规模并行GPU架构,该架构具有数千个并发线程,快速内存和内存合并以进行优化。我们分别使用带有第一和第二个聚类算法的仿真系统,在几个基准上展示了高达5倍和21倍的加速。我们的工作最终将大大减少整个设计周期。

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