With increasing core-count, the cache demand of modern processors has also increased.
However, due to strict area/power budgets and presence of poor data-locality workloads …

With the skyrocketing advances of process technology, the increased need to process huge
amount of data, and the pivotal need for power efficiency, the usage of Graphics Processing …

Graphics processing units (GPUs) have become essential general-purpose computing
platforms to accelerate a wide range of workloads, such as deep learning, scientific, and …

Graphics processing units (GPUs) include a large amount of hardware resources for parallel
thread executions. However, the resources are not fully utilized during runtime, and …

Modern GPU applications, such as machine learning (ML) frameworks, can only partially
utilize beefy GPUs, leading to GPU underutilization in cloud environments. Sharing GPUs …

The disparity between memory and CPU have been ameliorated by the introduction of
Network-on-Chip-based Chip-Multiprocessors (NoC-based CMPS). However, power …

Meeting the Quality of Service (QoS) requirement under task consolidation on the GPU is
extremely challenging. Previous work mostly relies on static task or resource scheduling and …

This paper proposes a new architecture, called Adaptive PREfetching and Scheduling
(APRES), which improves cache efficiency of GPUs. APRES relies on the observation that …

This paper proposes a new data prefetching technique for Graphics Processing Units
(GPUs) called Warp Aware Selective Prefetching (WASP). The main idea of WASP is to …

In this paper we present a distributed architecture to address the problems of managing,
tracking, and predicting astray person in large crowds. Our case study considers the …