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—Energy efficiency is a major concern in modern high-performance computing system design. In the past few years, there has been mounting evidence that power usage limits system scale and computing density, and thus, ultimately system performance. However, despite the impact of power and energy on the computer systems community, few studies provide insight(More)
Future high performance systems must use energy efficiently to achieve PFLOPS computational speeds and beyond. To address this challenge, we must first understand the power and energy characteristics of high performance computing applications. In this paper, we use a power-performance profiling framework called PowerPack to study the power and energy(More)
—Emergent heterogeneous systems must be optimized for both power and performance at exascale. Massive parallelism combined with complex memory hierarchies form a barrier to efficient application and architecture design. These challenges are exacerbated with GPUs as parallelism increases orders of magnitude and power consumption can easily double. Models(More)
Future large scale high performance supercomputer systems require high energy efficiency to achieve exaflops computational power and beyond. Despite the need to understand energy efficiency in high-performance systems, there are few techniques to evaluate energy efficiency at scale. In this paper, we propose a system-level iso-energy-efficiency model to(More)
—Support Vector Machine (SVM) has been widely used in data-mining and Big Data applications as modern commercial databases start to attach an increasing importance to the analytic capabilities. In recent years, SVM was adapted to the field of High Performance Computing for power/performance prediction, auto-tuning, and runtime scheduling. However, even at(More)
—With the increasing energy cost in data centers, an energy efficient approach to provide data intensive services in the cloud is highly in demand. This paper solves the energy cost reduction problem of data centers by formulating an energy-aware replica selection problem in order to guide the distribution of workload among data centers. The current popular(More)
—The power consumption of a large scale system ultimately limits its performance. Consuming less energy while preserving performance leads to better system utilization at scale. The iso-energy-efficiency model was proposed as a metric and methodology for explaining power and performance efficiency on scalable systems. For use in practice, we need to(More)
—The largest supercomputers in the world today consist of hundreds of thousands of processing cores and many more other hardware components. At such scales, hardware faults are a commonplace, necessitating fault-resilient software systems. While different fault-resilient models are available, most focus on allowing the computational processes to survive(More)
This paper presents novel cache optimizations for massively parallel, throughput-oriented architectures like GPUs. L1 data caches (L1 D-caches) are critical resources for providing high-bandwidth and low-latency data accesses. However, the high number of simultaneous requests from single-instruction multiple-thread (SIMT) cores makes the limited capacity of(More)
—Energy efficiency and resilience are two crucial challenges for High Performance Computing (HPC) systems to reach exascale. While energy efficiency and resilience issues have been extensively studied individually, little has been done to understand the interplay between energy efficiency and resilience for HPC systems. Decreasing the supply voltage(More)