Topic
Performance per watt
About: Performance per watt is a research topic. Over the lifetime, 315 publications have been published within this topic receiving 5778 citations.
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09 Aug 2013
TL;DR: This paper presents an application specific autonomic adaptive power and performance management system that utilizes AppFlow-based reasoning to configure dynamically datacenter resources and workload allocations and can reduce the VMs' power consumption up to 84% compared to static resource allocation and up to 30%Compared to other methods with minimum performance degradation.
Abstract: With the rapid growth of data centers and clouds, the power cost and power consumption of their computing and storage resources become critically important to be managed efficiently. Several research studies have shown that data servers typically operate at a low utilization of 10% to 15%, while their power consumption is close to those at peak loads. With this significant fluctuation in the workloads, an elastic delivery of computing services with an efficient power provisioning mechanism becomes an important design goal. Live workload migrations and virtualization are important techniques to optimize power and performance in large-scale data centers [5], [25] This paper presents an application specific autonomic adaptive power and performance management system that utilizes AppFlow-based reasoning to configure dynamically datacenter resources and workload allocations. This system will continuously monitor the workload to determine the current operating point of both workloads and the virtual machines (VMs) running these workloads and then predict the next operating points for these VMs. This enables the system to allocate the appropriate amount of hardware resources that can run efficiently the VM workloads with minimum power consumption. We have experimented with and evaluated our approach to manage the VMs running RUBiS bidding application. Our experimental results showed that our approach can reduce the VMs' power consumption up to 84% compared to static resource allocation and up to 30% compared to other methods with minimum performance degradation.
12 citations
TL;DR: By extending the Amdahl’s Law and the Karp-Flatt Metric, taking resilience into consideration, this article quantitatively model the integrated energy efficiency in terms of performance per Watt and showcases the trade-offs among typical HPC parameters.
Abstract: Ever-growing performance of supercomputers nowadays brings demanding requirements of energy efficiency and resilience, due to rapidly expanding size and duration in use of the large-scale computing systems. Many application/architecture-dependent parameters that determine energy efficiency and resilience individually have causal effects with each other, which directly affect the trade-offs among performance, energy efficiency and resilience at scale. To enable high-efficiency management for large-scale High-Performance Computing (HPC) systems nowadays, quantitatively understanding the entangled effects among performance, energy efficiency, and resilience is thus required. While previous work focuses on exploring energy-saving and resilience-enhancing opportunities separately, little has been done to theoretically and empirically investigate the interplay between energy efficiency and resilience at scale. In this article, by extending the Amdahl’s Law and the Karp-Flatt Metric, taking resilience into consideration, we quantitatively model the integrated energy efficiency in terms of performance per Watt and showcase the trade-offs among typical HPC parameters, such as number of cores, frequency/voltage, and failure rates. Experimental results for a wide spectrum of HPC benchmarks on two HPC systems show that the proposed models are accurate in extrapolating resilience-aware performance and energy efficiency, and capable of capturing the interplay among various energy-saving and resilience factors. Moreover, the models can help find the optimal HPC configuration for the highest integrated energy efficiency, in the presence of failures and applied resilience techniques.
12 citations
TL;DR: It is shown experimentally that GPU power consumption increases non-linearly with both temperature and supply voltage, as predicted by physical transistor models, and how automatic temperature-aware and application-dependent voltage and frequency scaling may provide a mechanism to achieve better power efficiency for a wider range of compute codes running on GPUs is discussed.
Abstract: The magnitude of the real-time digital signal processing challenge attached to large radio astronomical antenna arrays motivates use of high performance computing (HPC) systems. The need for high power efficiency (performance per watt) at remote observatory sites parallels that in HPC broadly, where efficiency is an emerging critical metric. We investigate how the performance per watt of graphics processing units (GPUs) is affected by temperature, core clock frequency and voltage. Our results highlight how the underlying physical processes that govern transistor operation affect power efficiency. In particular, we show experimentally that GPU power consumption grows non-linearly with both temperature and supply voltage, as predicted by physical transistor models. We show lowering GPU supply voltage and increasing clock frequency while maintaining a low die temperature increases the power efficiency of an NVIDIA K20 GPU by up to 37-48% over default settings when running xGPU, a compute-bound code used in radio astronomy. We discuss how temperature-aware power models could be used to reduce power consumption for future HPC installations. Automatic temperature-aware and application-dependent voltage and frequency scaling (T-DVFS and A-DVFS) may provide a mechanism to achieve better power efficiency for a wider range of codes running on GPUs
12 citations
TL;DR: This work modify user space libraries and device drivers of GPUs and the InfiniBand network device in a way to enable the GPU to control an Infini Band network device to independently source and sink communication requests without any involvement of the CPU.
Abstract: Due to their massive parallelism and high performance per Watt, GPUs have gained high popularity in high-performance computing and are a strong candidate for future exascale systems. But communicat...
12 citations
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01 Jan 2012
TL;DR: It is argued that performance per watt, which is often cited in the graphics hardware industry, is not a particularly useful unit for power efficiency in scientific and engineering discussions, and joules per task and watts are more reasonable units.
Abstract: In this short note, we argue that performance per watt, which is often cited in the graphics hardware industry, is not a particularly useful unit for power efficiency in scientific and engineering discussions. We argue that joules per task and watts are more reasonable units. We show a concrete example where nanojoules per pixel is much more intuitive, easier to compute aggregate statistics from, and easier to reason about.
12 citations