With the rapid growth of program scale, program analysis, maintenance and optimization
become increasingly diverse and complex. Applying learning-assisted methodologies onto …

Caching techniques are widely used in today's computing infrastructure from virtual memory
management to server cache and memory cache. This paper builds on two observations …

Convolution Neural Networks (CNNs), a special subcategory of Deep Learning Neural
Networks (DNNs), have become increasingly popular in industry and academia for their …

Caching techniques are widely used in the era of cloud computing from applications, such
as Web caches to infrastructures, Memcached and memory caches in computer …

Caching techniques are widely used in the era of cloud computing from applications, such
as Web caches to infrastructures, Memcached and memory caches in computer …

Non-Uniform Memory Access (NUMA) architecture imposes numerous performance
challenges to today's cloud workloads. Due to the complexity and the massive scale of …

In this paper we present a machine-learning approach to predict the impact on performance
of core and memory placement in non-uniform memory access (NUMA) systems. The impact …

In this paper, we predict memory bandwidth in NUMA architecture by implementing a
method based on a supervised machine learning algorithm, the k-Nearest Neighbor (KNN) …

Memory allocation is increasingly important to parallel performance, yet it is challenging
because a program has data of many sizes, and the demand differs from thread to thread …

Abstract High Performance Computing (HPC) has delivered tremendous improvements in
scientific applications these days, much of which can be attributed to the development of …