Fat versus Thin Threading Approach on GPUs: Application to Stochastic Simulation of Chemical Reactions
Fat versus Thin Threading Approach on GPUs: Application to Stochastic Simulation of Chemical Reactions
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DOI：
10.1109/tpds.2011.157
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发表时间：
2012-02-01
影响因子：
5.3
通讯作者：
Maini, Philip K.
中科院分区：
文献类型：
--
作者：
Klingbeil, Guido;Erban, Radek;Maini, Philip K.
我们探讨两种不同的线程的图形处理单元（GPU）上的方法，利用当前GPU架构的两个不同的特点。胖线程方法试图通过依赖共享内存和寄存器来最小化数据访问时间，这可能会牺牲并行性。瘦线程方法最大化并行性并试图隐藏访问延迟。我们使用吉莱斯皮[14]的随机模拟算法（SSA）将这两种方法应用于化学反应系统的并行随机模拟。在这些情况下，所提出的细线程的方法显示出相当的性能，同时消除了反应系统的大小的限制。
We explore two different threading approaches on a graphics processing unit (GPU) exploiting two different characteristics of the current GPU architecture. The fat thread approach tries to minimize data access time by relying on shared memory and registers potentially sacrificing parallelism. The thin thread approach maximizes parallelism and tries to hide access latencies. We apply these two approaches to the parallel stochastic simulation of chemical reaction systems using the stochastic simulation algorithm (SSA) by Gillespie [14]. In these cases, the proposed thin thread approach shows comparable performance while eliminating the limitation of the reaction system's size.