Auto-tuning for neural simulations on different parallel hardware

自动调整不同并行硬件上的神经模拟

• 批准号: 313818712
• 负责人: Professor Dr.-Ing. Fred Henrik Hamker
• 金额: --
• 依托单位: Professur Künstliche Intelligenz
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/313818712?language=en
• 关键词: Auto; tuning; neural; simulations; different

Biologically-realistic neural simulations pose new challenges to parallel computing because of the dependency between the network activity and the total computation time, the temporal evolution of their internal connectivity and the heterogeneity of the networks. Auto-tuning techniques can help selecting optimizations at various levels (algorithmic, data structures) using code generation. In this project, we will investigate online and offline auto-tuning methods to increase the parallel efficiency of neural simulations on different hardware (shared-memory, distributed or GPU-based systems), focusing particularly on the improvement of performance measurements, the analysis of data structures, distribution of data using graph partitioning, the learning of heuristics to restrict the search space and parameterized code generation at run-time for a particular network. The resulting mechanisms will be integrated in an existing neural simulator.

由于网络活动与总计算时间之间的依赖性、内部连通性的时间演化以及网络的异构性，生物真实感神经模拟对并行计算提出了新的挑战。自动调优技术可以使用代码生成来帮助选择不同级别(算法、数据结构)的优化。在这个项目中，我们将研究在线和离线自动调整方法，以提高不同硬件(共享内存、分布式或基于GPU的系统)上的神经模拟的并行效率，重点放在性能测量的改进、数据结构的分析、使用图划分的数据分布、限制搜索空间的启发式学习以及特定网络运行时的参数化代码生成。由此产生的机制将被集成到现有的神经模拟器中。