Energy efficiency has become one of the top design criteria for current computing systems. The Dynamic Voltage and Frequency Scaling （DVFS） has been widely adopted by laptop computers, servers, and mobile devices to...Energy efficiency has become one of the top design criteria for current computing systems. The Dynamic Voltage and Frequency Scaling （DVFS） has been widely adopted by laptop computers, servers, and mobile devices to conserve energy, while the GPU DVFS is still at a certain early age. This paper aims at exploring the impact of GPU DVFS on the application performance and power consumption, and furthermore, on energy conservation. We survey the state-of-the-art GPU DVFS characterizations, and then summarize recent research works on GPU power and performance models. We also conduct real GPU DVFS experiments on NVIDIA Fermi and Maxwell GPUs. According to our experimental results, GPU DVFS has significant potential for energy saving. The effect of scaling core voltage/frequency and memory voltage/frequency depends on not only the GPLI architectures, but also the characteristic of GPU applications.展开更多
Large-scale key-value stores are widely used in many Web-based systems to store huge amount of data as(key, value) pairs. In order to reduce the latency of accessing such(key, value) pairs, an in-memory cache system i...Large-scale key-value stores are widely used in many Web-based systems to store huge amount of data as(key, value) pairs. In order to reduce the latency of accessing such(key, value) pairs, an in-memory cache system is usually deployed between the front-end Web system and the back-end database system. In practice, a cache system may consist of a number of server nodes, and fault tolerance is a critical feature to maintain the latency Service-Level Agreements(SLAs). In this paper, we present the design, implementation, analysis, and evaluation of R-Memcached, a reliable in-memory key-value cache system that is built on top of the popular Memcached software. R-Memcached exploits coding techniques to achieve reliability, and can tolerate up to two node failures.Our experimental results show that R-Memcached can maintain very good latency and throughput performance even during the period of node failures.展开更多
文摘Energy efficiency has become one of the top design criteria for current computing systems. The Dynamic Voltage and Frequency Scaling （DVFS） has been widely adopted by laptop computers, servers, and mobile devices to conserve energy, while the GPU DVFS is still at a certain early age. This paper aims at exploring the impact of GPU DVFS on the application performance and power consumption, and furthermore, on energy conservation. We survey the state-of-the-art GPU DVFS characterizations, and then summarize recent research works on GPU power and performance models. We also conduct real GPU DVFS experiments on NVIDIA Fermi and Maxwell GPUs. According to our experimental results, GPU DVFS has significant potential for energy saving. The effect of scaling core voltage/frequency and memory voltage/frequency depends on not only the GPLI architectures, but also the characteristic of GPU applications.
基金supported in part by Hong Kong GRF grant HKBU 210412 and HKBU grant FRG2/14-15/059
文摘Large-scale key-value stores are widely used in many Web-based systems to store huge amount of data as(key, value) pairs. In order to reduce the latency of accessing such(key, value) pairs, an in-memory cache system is usually deployed between the front-end Web system and the back-end database system. In practice, a cache system may consist of a number of server nodes, and fault tolerance is a critical feature to maintain the latency Service-Level Agreements(SLAs). In this paper, we present the design, implementation, analysis, and evaluation of R-Memcached, a reliable in-memory key-value cache system that is built on top of the popular Memcached software. R-Memcached exploits coding techniques to achieve reliability, and can tolerate up to two node failures.Our experimental results show that R-Memcached can maintain very good latency and throughput performance even during the period of node failures.
