Spatial multi-programming is one of the most efficient multi-programming methods on Graphics Processing Units (GPUs). This multi-programming scheme generates variety in resource requirements of stream multiprocessors (SMs) and creates opportunities for sharing unused portions of each SM resource with other SMs. Although this approach drastically improves GPU performance, in some cases it leads ...

We present a multi-GPU design, implementation and performance evaluation of the Halevi-Polyakov-Shoup (HPS) variant Fan-Vercauteren (FV) levelled Fully Homomorphic Encryption (FHE) scheme. Our design follows data parallelism approach uses partitioning methods to distribute workload in FV primitives evenly across available GPUs. The is put address space runtime requirements FHE computations. It ...

This paper introduces an empirical, perceptually-based method which exploits the temporal coherence in consecutive frames to reduce the CPU-GPU traffic size during real-time visualization of time-varying volume data. In this new scheme, a multi-threaded CPU mechanism simulates GPU pre-rendering functions to characterize the local behaviour of the volume. These functions exploit the temporal coh...

This paper proposed the novel rendering architecture based on GPU massive multithreading for generating multi-view content to 3D display. With the input of 2D plus depth images, this system can support stereoscopic display as well as auto-stereoscopic one. In order to achieve real-time and high quality depth image based rendering (DIBR), anti-aliasing pixel rendering, unoccupied pixel inpaintin...

We present the GPU Module of a Performance Prediction Toolkit developed at Los Alamos National Laboratory, which enables code developers to efficiently test novel algorithmic ideas particularly for large-scale computational physics codes. The GPU Module is a heavily-parameterized model of the GPU hardware that takes as input a sequence of abstracted instructions that the user provides as a repr...

This paper describes the design of a hierarchical parallel algorithm for accelerating community detection which involves partitioning a network into communities of densely connected nodes. The algorithm is based on the Louvain method developed at the Université Catholique de Louvain, which uses modularity to measure community quality and has been successfully applied on many different types of ...

We present the newly developed code, GAMER (GPU-accelerated Adaptive MEsh Refinement code), which has adopted a novel approach to improve the performance of adaptive mesh refinement (AMR) astrophysical simulations by a large factor with the use of the graphic processing unit (GPU). The AMR implementation is based on a hierarchy of grid patches with an oct-tree data structure. We adopt a three-d...

High performance computing with low cost machines becomes a reality with GPU. Unfortunately, high performances are achieved when the programmer exploits the architectural specificities of the GPU processors: he has to focus on inter-GPU communications, task allocations among the GPUs, task scheduling, external memory prefetching, and synchronization. In this paper, we propose and evaluate a com...