CSR: Small: Collaborative Research: Pursuing High Performance on Clouds and Other Dynamically Heterogeneous Computing Platforms

新兴技术带来了革命性的计算平台，如计算云和许多类型的计算网格。这些平台承诺为公众提供高性能的计算平台。然而，实现这一承诺需要人们应对平台的动态异质性，即它们组成的计算机的相对能力和速度可能在不可预测的时间以不可预测的方式发生变化；例如，由于工作负载的不可预测的变化，共享计算机可能会显著减慢或加快速度。pi的研究正在开发一种变革性的计算范式，使这些平台上的高性能计算成为可能。新范式取代了传统调度器试图适应计算平台的细节——这是一个动态异质性所反驳的目标——通过编排一个复杂的计算，尊重计算固有结构的相关细节。通过这种方式，范式增加了并行（即同时）执行独立任务的机会，从而更快地完成计算。初步评估——通过与普通计算调度器的模拟竞争——表明，新范式完成计算的速度通常比竞争策略快两位数。挑战是以一种计算效率高的方式实现范式，而pi正在追求实现这种效率的各种途径。由于动态异构平台的复杂性，这一目标需要调度算法和仿真软件技术的进步。由于他们在算法，应用和系统方面的互补专业知识，pi具有独特的资格来产生这些进步。

Emerging technologies have led to revolutionary computing platformssuch as computing clouds and many genres of computing grids. Theseplatforms promise to make high-performance computing platformsaccessible to the public. Realizing this promise, though, requiresone to cope with the platforms' dynamic heterogeneity, i.e., the factthat their constituent computers' relative powers and speeds canchange at unpredictable times and in unpredictable ways; for instance,shared computers may slow down or speed up significantly because ofunpredictable changes in workloads. The research of the PIs isdeveloping a transformative computing paradigm that will enablehigh-performance computing on these platforms. The new paradigmreplaces traditional schedulers' attempts to accommodate theparticulars of a computing platform---a goal that dynamicheterogeneity confutes---by orchestrating a complex computation in away that honors the relevant details of the computation's inherentstructure. In this way, the paradigm increases opportunities forexecuting independent tasks in parallel (i.e., simultaneously),thereby completing computations faster. Preliminary assessments---viasimulated competitions with common computation schedulers---suggestthat the new paradigm often completes computations faster thancompeting strategies by double-digit percentages. The challenge is torealize the paradigm in a computationally efficient manner, and thePIs are pursuing a variety of avenues toward achieving suchefficiency. Because of the complexity of dynamically heterogeneousplatforms, this goal requires advances in the technology of bothscheduling algorithms and simulation software. The PIs are uniquelyqualified to produce such advances because of their complementaryexpertise in algorithms, applications, and systems.