CAREER: Rethinking HPC Resilience in the Exascale Era

弹性是高性能计算（HPC）中的一个关键的亿级研究挑战。由于错误率高得多，兆级超级计算机在计算中几乎没有进展，或者可能由于故障而产生不正确的结果，从而使兆级性能变得无用。 挑战是如何在不增加性能开销、功耗和底层硬件复杂性的情况下，实现兆级的完整HPC弹性。 为此，本研究项目设计并开发了低成本的硬件/软件协同技术，以实现兆兆级时代的HPC弹性。该项目涉及四个研究目标：（1）低成本的CPU软错误容错;智能编译器-架构交互可以验证错误的缺失并执行细粒度恢复，从而消除SDC。 （2）针对商用GPU的编译器指导的软错误恢复;它可以从GPU寄存器文件中删除功耗高的纠错码（ECC）逻辑，而不会影响其恢复能力。 (3)轻量级非易失性存储器（NVM）持久性;它可以减轻传统重量级HPC检查点的开销，并支持应用程序的全系统持久性，而无需可中断操作。 (4)低成本的时序误差弹性，用于积极的电压缩放，以最大限度地提高能源效率，并保证程序的正确性。由此产生的人工制品和技术，预计将有助于通过解决建设可靠的HPC系统的挑战，提高国家的竞争力。 该研究成果影响了广泛的任何学科，这些学科需要正确的计算结果，因此需要从嵌入式系统到HPC云的可靠计算系统。因此，使用所提出的技术将使当前和新兴的应用程序的执行更加可靠，因此直接影响我们的生活方式。本研究项目将产生三种类型的数据：（1）算法和模型，（2）软件原型，（3）测试基础设施，包括模拟器和评估基准及其跟踪，（4）教育材料。我们所有的软件工具都将是开源的，并向公众、实验室和工业界开放。

Resilience is one of the key exascale research challenges in high-performancecomputing (HPC). Due to much high error rates, exascale supercomputers couldmake little progress in computations, or might generate incorrect results due tofailures, rendering the exascale performance useless. Thechallenge is how to achieve a complete HPC resilience at exascale in a way thatdoes not increase the performance overhead, the power consumption, and thecomplexity of underlying hardware. To this end, this research project designsand develops low-cost hardware/software cooperative techniques for HPCresilience in the exascale era. This project involves four research goals: (1) low-cost soft error resiliencefor CPUs; intelligent compiler-architecture interaction can validate the lack oferrors and performs fine-grained recovery, thus eliminating SDC. (2)compiler-directed soft error resilience for commodity GPUs; it can remove thepower-hungry error-correcting code (ECC) logic from the GPU register fileswithout compromising their resilience. (3) lightweight nonvolatile memory (NVM)persistence; it can mitigate the overhead of traditional heavyweight HPCcheckpointing and support whole-system persistence for applications withoutirrevocable operations. (4) low-cost timing error resilience for aggressivevoltage scaling to maximize the energy-efficiency with program correctnessguarantee.The resulting artifacts and technologies are expected to contribute to thenation's competitiveness by addressing the challenge of building reliable HPCsystems. The research outcome impacts a broad range of any disciplines thatneed correct computation results thus requiring reliable computing systemscovering from embedded systems to HPC cloud. Consequently, use of the proposedtechniques will make the execution of current and emerging applications muchmore reliable, and therefore directly affect our way of life.There will be three types of data generated from this research project: (1)algorithms and models, (2) software prototype, (3) testing infrastructureincluding simulators and evaluation benchmarks and their traces, (4) educationalmaterials. All of our software tools will be open source and made available tothe public, laboratories and industry.