Fault Table Computation on GPUs
Fault Table Computation on GPUs
复制标题
GPU 上的故障表计算
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
S. Khatri
中科院分区：
文献类型：
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作者：
Kanupriya Gulati;S. Khatri
在本文中，我们探讨了故障表生成的图形处理单元（GPU）上的实现。在VLSI测试和调试中，故障表是进行故障诊断和故障检测的基础。生成故障表需要大量的故障模拟，没有故障丢弃，并且从计算的角度来看是极其昂贵的。故障模拟本质上是可并行的，并且GPU可以并行操作的大量线程可以用于加速故障模拟，从而加速故障表生成。我们的方法，称为GFTABLE，采用了模式并行的方法，利用位并行和线程级并行。我们的实现是一个显着修改版本的FSIM，这是模式并行故障模拟方法的单核处理器。像FSIM一样，GFTABLE利用关键路径跟踪和支配者概念来减少运行时间。对FSIM的进一步修改使我们能够最大限度地利用GPU的巨大内存带宽和高计算能力。我们的方法不将电路（或电路的任何部分）存储在GPU上。高效的并行约简操作实现在我们的实现GFTABLE。我们将我们的性能与FSIM* 进行了比较，后者经过FSIM修改，可在单核处理器上生成故障表。我们的实验表明，在单个NVIDIA Quadro FX 5800 GPU卡上实现的GFTABLE可以生成50万个测试模式的故障表，与FSIM* 相比，平均快15.68倍。使用NVIDIA Tesla服务器，我们的方法可能会快89.57倍。
In this paper, we explore the implementation of fault table generation on a Graphics Processing Unit (GPU). A fault table is essential for fault diagnosis and fault detection in VLSI testing and debug. Generating a fault table requires extensive fault simulation, with no fault dropping, and is extremely expensive from a computational standpoint. Fault simulation is inherently parallelizable, and the large number of threads that a GPU can operate on in parallel can be employed to accelerate fault simulation, and thereby accelerate fault table generation. Our approach, called GFTABLE, employs a pattern parallel approach which utilizes both bit-parallelism and thread-level parallelism. Our implementation is a significantly modified version of FSIM, which is pattern parallel fault simulation approach for single core processors. Like FSIM, GFTABLE utilizes critical path tracing and the dominator concept to reduce runtime. Further modifications to FSIM allow us to maximally harness the GPU’s huge memory bandwidth and high computational power. Our approach does not store the circuit (or any part of the circuit) on the GPU. Efficient parallel reduction operations are implemented in our implementation of GFTABLE. We compare our performance to FSIM*, which is FSIM modified to generate a fault table on a single core processor. Our experiments indicate that GFTABLE, implemented on a single NVIDIA Quadro FX 5800 GPU card, can generate a fault table for 0.5 million test patterns on average 15.68× faster when compared with FSIM*. With the NVIDIA Tesla server, our approach would be potentially 89.57× faster.