high performance computing on graphics processing units: hgpu.org

The ability to timely process significant amounts of continuously updated spatial data is mandatory for an increasing number of applications. Parallelism enables such applications to face this data-intensive challenge and allows the devised systems to feature low latency and high scalability. In this paper we focus on a specific data-intensive problem, concerning the repeated processing […]

The graphics processing unit (GPU) has seen significant increase in performance over the past few years. Hence the interest in using GPUs for more general purposes has increased. The higher number of cores on a GPU allows it to outperform central processing units (CPUs). However, since in certain aspects instructions executed on the GPU must […]

Recent work has explored using higher level languages to improve programmer productivity on GPUs. These languages often utilize high level computation patterns (e.g., Map and Reduce) that encode parallel semantics to enable automatic compilation to GPU kernels. However, the problem of efficiently mapping patterns to GPU hardware becomes significantly more difficult when the patterns are […]

Runtime verification is an effective automated method for specification-based offline testing and analysis as well as online monitoring of complex systems. The specification language is often a variant of regular expressions or a popular temporal logic, such as LTL. This paper presents a novel and efficient parallel algorithm for verifying a more expressive version of […]

A micromagnetic simulator running on graphics processing unit (GPU) is presented. It achieves significant performance boost as compared to previous central processing unit (CPU) simulators, up to two orders of magnitude for large input problems. Different from GPU implementations of other research groups, this simulator is developed with C++ Accelerated Massive Parallelism (C++ AMP) and […]

Kepler GTX Titan Black and Kepler Tesla K40 are still the best GPUs for high performance computing, although Maxwell GPUs such as GTX 980 are available in the market. Hence, we measure the performance of our lattice QCD codes using the Kepler GPUs. We also upgrade our code to use the latest CPS (Columbia Physics […]

A popular approach to programming manycore GPUs is the Single Instruction Multiple Thread (SIMT) abstraction. SIMT has the benefit of presenting a "single thread" view, alleviating the complexity of explicitly vectorizing the source code. However, due to the SIMD nature of the underlying hardware it is often difficult to fully hide all aspects from the […]

Embedding real-time 3D reconstruction of a scene from a low-cost depth sensor can improve the development of technologies in the domains of augmented reality, mobile robotics, and more. However, current implementations require a computer with a powerful GPU, which limits its prospective applications with low-power requirements. To implement low-power 3D reconstruction we embedded two prominent […]

Effective utilization of GPU processing capacity for scientific workloads is often limited by memory throughput and PCIe communication transfer times. This is particularly true for semi-analytic Fourier-domain computations in earthquake modeling (Relax) where operations on large-scale 3D data structures can require moving large volumes of data from storage to the compute in predictable but orthogonal […]

In this paper, we describe GPU-Eclat algorithm, a GPU (General Purpose Graphics Processing Unit) enhanced implementation of Frequent Item set Mining (FIM). The frequent itemsets are extracted from a transactional database as it is a essential assignment in data mining field because of its broad applications in mining association rules, time series, correlations etc. The […]

Deep learning has enjoyed tremendous success in recent years. Unfortunately, training large models can be very time consuming, even on GPU hardware. We describe a set of extensions to the state of the art Caffe library [3], allowing training on multiple threads and GPUs, and across multiple machines. Our focus is on architecture, implementing asynchronous […]

This dissertation presents research focusing on reshaping the design paradigm of electronic design automation (EDA) applications to embrace the computational throughput of a massively parallel computing architecture. The EDA industry has gone through major evolution in algorithm designs over the past several decades, delivering improved and more sophisticated design tools. Today, these tools provide a […]