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首页> 外文会议>IEEE International System-on-Chip Conference >Power- Thermal Aware Balanced Task-Resource Co-Allocation in Heterogeneous Many CPU-GPU Cores NoC in Dark Silicon Era
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Power- Thermal Aware Balanced Task-Resource Co-Allocation in Heterogeneous Many CPU-GPU Cores NoC in Dark Silicon Era

机译：在黑暗硅时代的异构许多CPU-GPU核心Noc中的电力感知均衡任务资源共同分配

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摘要

To provide balanced mapping for multiple applications in many-core heterogeneous CPU-GPU systems on network-on-chip (NoC) in dark silicon era, power-thermal aware task-resource co-allocation with reconfigurable NoC framework is proposed in this work. Task allocation and resource configuration problem is initially formulated using linear programming (LP) optimization model to search for an optimal solution. Distributed resource management in heterogeneous CPU-GPU systems with a fast balanced mapping heuristic is proposed to minimize power and thermal hotspots and improve performance at run-time while meeting the computation, communication, power and thermal budgets constraints of manycore NoC in dark silicon. We have also implemented a state-of-the-art minimum-path contiguous mapping for comparisons. Simulations under real-world benchmarks and platforms show that the proposed dynamic load-balanced mapping strategy improves NoC latency and throughput by 50-100% (while providing near-optimal solution) compared to minimum-path.
机译：为在暗硅时代的片上芯片（NOC）上的多核异构CPU-GPU系统中提供平衡映射，在黑暗的硅时代，在这项工作中提出了与可重构的NOC框架的电力感知任务资源共同分配。任务分配和资源配置问题首先使用线性编程（LP）优化模型来搜索最佳解决方案。建议在具有快速平衡映射启发式的异构CPU-GPU系统中的分布式资源管理，以最大限度地减少功率和热热点，并在符合黑暗硅中的数核NOC的计算，通信，电力和热预算限制时提高运行时的性能。我们还实现了用于比较的最先进的最低路径连续映射。与最小路径相比，实际基准和平台下的仿真提出了建议的动态负荷 - 平衡映射策略将NOC延迟和吞吐量提高了50-100％（同时提供近最佳解决方案）。

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