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Tensor contraction is a fundamental operation in many algorithms with a plethora of applications ranging from quantum chemistry over fluid dynamics and image processing to machine learning. The performance of tensor computations critically depends on ...

Because of severe limitations in technology scaling, architects have innovated in specializing general purpose processors for computation primitives (e.g. vector instructions, loop accelerators). The general principle is exposing rich semantics to the ...

Network traffic for delay-sensitive services has become a dominant part in the network. Proactive caching with the aid of predictive information has been proposed as a promising method to enhance delay performance. In this paper, we analytically ...

Data and services provided by shared facilities, such as large-scale observing facilities, have become important enablers of scientific insights and discoveries across many science and engineering disciplines. Ensuring satisfactory quality of service ...

Network traffic of delay-sensitive services has become a dominant part in the network. Proactive caching with the aid of predictive information has been proposed as a promising method to enhance the delay performance, which is one of the principal ...

In this paper, we present Deepcache a novel Framework for content caching, which can significantly boost cache performance. Our Framework is based on powerful deep recurrent neural network models. It comprises of two main components: i) Object ...

New hardware features have propelled designs and analysis in main-memory hash joins. In previous studies, memory access has always been the primary bottleneck for hash join algorithms. However, there are relatively few studies devoted to bottlenecks ...

Value prediction improves instruction level parallelism in superscalar processors by breaking true data dependencies. Although this technique can significantly improve overall performance, most of the state-of-the-art value prediction approaches require ...

Graph processing is increasingly bottlenecked by main memory accesses. On-chip caches are of little help because the irregular structure of graphs causes seemingly random memory references. However, most real-world graphs offer significant potential ...

The recent deceleration of Moore's law bespeaks new approaches for optimization of resources. Machine learning has been applied to a wide variety of problems across multiple domains; however, the space of machine learning research for storage ...

Network latency in mobile software has a large impact on user experience, with potentially severe economic consequences. Prefetching and caching have been shown effective in reducing the latencies in browser-based systems. However, those techniques ...

Prefetching is a well-known technique to mitigate scalability challenges in the Partitioned Global Address Space (PGAS) model. It has been studied as either an automated compiler optimization or a manual programmer optimization. Using the PGAS locality ...

In this paper, we present DEEPCACHE a novel Framework for content caching, which can significantly boost cache performance. Our Framework is based on powerful deep recurrent neural network models. It comprises of two main components: i) Object ...

Nowadays, news-based websites and portals provide significant amounts of multimedia content to accompany news stories and articles. Within this context, HTTP Adaptive Streaming is generally used to deliver video over the best-effort Internet, allowing ...

This paper shows that in the presence of data prefetchers, cache replacement policies are faced with a large unexplored design space. In particular, we observe that while Belady's MIN algorithm minimizes the total number of cache misses---including ...

On-die caches are a popular method to help hide the main memory latency. However, it is difficult to build large caches without substantially increasing their access latency, which in turn hurts performance. To overcome this difficulty, on-die caches ...

Prefetching is a central component in most microarchitectures. Many different algorithms have been proposed with varying degrees of complexity and effectiveness. There are inherent tradeoffs among various metrics especially when we try to exploit both ...

The goal of this study is to optimize energy efficiency of database clusters through prefetching and caching strategies. We design a workload-skewness scheme to collectively manage a set of hot and cold nodes in a database cluster system. The prefetching ...

The importance of irregular applications such as graph analytics is rapidly growing with the rise of Big Data. However, parallel graph workloads tend to perform poorly on general-purpose chip multiprocessors (CMPs) due to poor cache locality, low ...

Many modern workloads compute on large amounts of data, often with irregular memory accesses. Current architectures perform poorly for these workloads, as existing prefetching techniques cannot capture the memory access patterns; these applications end ...