high performance computing on graphics processing units: hgpu.org

Merging and sorting algorithms are the backbone of many modern computer applications. As such, efficient implementations are desired. Recent architectural advancements in CPUs (Central Processing Units), such as wider and more powerful vector instructions, allow for algorithmic improvements. This paper presents a new approach to merge sort using vector instructions. Traditional approaches to vectorized sorting […]

It is universally accepted that the performance of graph algorithms is heavily dependent on the algorithm, the execution platform, and the structure of the input graph. This variability remains difficult to predict and hinders the choice of the right algorithm for a given problem. In this work, we focus on a case study: breadth-first search […]

In recent years, the traditional approaches for improving performance, such as increasing the clock frequency, has come to a dead-end. To tackle this issue, parallel architectures, such as multi-/many-core processors, have been envisioned to increase the performance by providing greater processing capabilities. However, programming efficiently for this architectures demands big efforts in order to transform […]

In stream processing, stream join is one of the critical sources of performance bottlenecks. The sliding-window-based stream join provides a precise result but consumes considerable computational resources. The current solutions lack support for the join predicates on large windows. These algorithms and their hardware accelerators are either limited to equi-join or use a nested loop […]

This project investigates how different approaches to parallel optimization impact the performance portability for Fortran codes. In addition, we explore the productivity challenges due to the software tool-chain limitations unique to Fortran. For this study, we build upon the Truchas software, a metal casting manufacturing simulation code based on unstructured mesh methods and our initial […]

While IoT is becoming widespread, cyber security of its devices is still a limiting factor where recent attacks (e.g., the Mirai botnet) underline the need for countermeasures. One commonly-used security mechanism is a Network Intrusion Detection System (NIDS), but the processing need of NIDS has been a significant bottleneck for large dedicated machines, and a […]

The performance and efficiency of distributed training of Deep Neural Networks highly depend on the performance of gradient averaging among all participating nodes, which is bounded by the communication between nodes. There are two major strategies to reduce communication overhead: one is to hide communication by overlapping it with computation, and the other is to […]

The efficacy of deep learning has resulted in it becoming one of the most important applications run in data centers today. The NVIDIA Tesla V100 GPU introduced a specialized functional unit called the Tensor Core to meet growing demand for higher performance on this workload. To exploit the full capability of current NVIDIA GPUs machine […]

BACKGROUND: The Smith-Waterman (SW) algorithm is the best choice for searching similar regions between two DNA or protein sequences. However, it may become impracticable in some contexts due to its high computational demands. Consequently, the computer science community has focused on the use of modern parallel architectures such as Graphics Processing Units (GPUs), Xeon Phi […]

One line of development followed in the field of supercomputing is the use of specific purpose processors to speed up certain types of computations. In this thesis we study the use of graphics processing units as computer accelerators and apply it to the field of linear algebra. In particular, we work with the SLEPc library […]

This paper envisions a future in which high performance and energy-modest parallel computing on low-end edge devices were achieved through cross-device functionality abstraction to make them interactive to cloud machines. Rather, there has been little exploration of the overall optimization into kernel processing can deliver for increasingly popular but heavy burden on low-end edge devices. […]

Recent characterizations of data movement performance have evaluated optimizations for dense and blocked accesses used by accelerators like GPUs and Xeon Phi, but sparse access patterns like scatter and gather are still not well understood across current and emerging architectures. We propose a tunable benchmark suite, Spatter, that allows users to characterize scatter, gather, and […]