We present and evaluate the TILA-rin GPU microarchitecture for embedded systems using the ATTILA GPU simulation framework. We use a trace from an execution of the Unreal Tournament 2004 PC game to evaluate and compare the performance of the proposed embedded GPU against a baseline GPU architecture for the PC. We evaluate the different elements that have been removed from the baseline GPU archit...

In this study computations of the two-dimensional Direct Simulation Monte Carlo (DSMC) method using Graphics Processing Units (GPUs) are presented. An all-device (GPU) computational approach is adopted – where the entire computation is performed on the GPU device, leaving the CPU idle which includes particle moving, indexing, collisions between particles and state sampling. The subsequent appli...

In recent years, more and more transistors have been integrated within the GPU, which has resulted in steadily rising power consumption requirements. In this paper we present a preliminary scheme to statistically analyze and model the power consumption of a mainstream GPU (NVidia GeForce 8800gt) by exploiting the innate coupling among power consumption characteristics, runtime performance, and ...

In this article, the graphics processing unit (GPU)-accelerated CatBoost (GPU-CatBoost) algorithm for hyperspectral image classification is first introduced and comparatively studied using diverse features. To further foster performance from both accurate efficient viewpoints, an ensemble version of GPU-CatBoost, GPU-accelerated CatBoost-Forest (GPU-CatBF) algorithm, proposed by adopting parall...

We propose a server-based approach to manage a general-purpose graphics processing unit (GPU) in a predictable and efficient manner. Our proposed approach introduces a GPU server that is a dedicated task to handle GPU requests from other tasks on their behalf. The GPU server ensures bounded time to access the GPU, and allows other tasks to suspend during their GPU computation to save CPU cycles...

Graphics processing units (GPUs) provide orders-ofmagnitude speedup for compute-intensive data-parallel applications. However, enterprise and cloud computing domains, where resource isolation of multiple clients is required, have poor access to GPU technology. This is due to lack of operating system (OS) support for virtualizing GPUs in a reliable manner. To make GPUs more mature system citizen...

The graphics processing unit (GPU) continues to make significant strides as an accelerator in commodity cluster computing for high-performance computing (HPC). For example, three of the top five fastest supercomputers in the world, as ranked by the TOP500, employ GPUs as accelerators. Despite this increasing interest in GPUs, however, optimizing the performance of a GPU-accelerated compute node...