high performance computing on graphics processing units: hgpu.org

Dense Multi-GPU systems have recently gained a lot of attention in the HPC arena. Traditionally, MPI runtimes have been primarily designed for clusters with a large number of nodes. However, with the advent of MPI+CUDA applications and CUDA-Aware MPI runtimes like MVAPICH2 and OpenMPI, it has become important to address efficient communication schemes for such […]

FFT (fast Fourier transform) plays a very important role in many fields, such as digital signal processing, digital image processing and so on. However, in application, FFT becomes a factor of affecting the processing efficiency, especially in remote sensing, which large amounts of data need to be processed with FFT. So shortening the FFT computation […]

It has been shown that increasing model depth improves the quality of neural machine translation. However, different architectural variants to increase model depth have been proposed, and so far, there has been no thorough comparative study. In this work, we describe and evaluate several existing approaches to introduce depth in neural machine translation. Additionally, we […]

The community needs simpler mechanisms to access the performance available in accelerators, such as GPUs, FPGAs, and APUs, due to their increasing use in stateof-the-art supercomputers. Programming models like CUDA, OpenMP, OpenACC and OpenCL can efficiently offload compute-intensive workloads to these devices. By default these models naively offload computation without overlapping it with communication (copying […]

Today, with the growth of highly parallel and heterogeneous architectures, systems composed of a combination of multicore CPUs, GPUs, and accelerators are becoming more common in HPC. Although heterogeneous architectures bring considerable benefits from a performance and energy perspective, they also make application development very challenging introducing the necessity of different parallel programming paradigms. Recently, […]

The availability of digital X-ray detectors, together with advances in reconstruction algorithms, creates an opportunity for bringing 3D capabilities to conventional radiology systems. The downside is that reconstruction algorithms for non-standard acquisition protocols are generally based on iterative approaches that involve a high computational burden. The development of new flexible X-ray systems could benefit from […]

With the growing complexity of software, the number of test cases needed for effective validation is extremely large. Executing these large test suites is expensive and time consuming, putting an enormous pressure on the software development cycle. In previous work, we proposed using Graphics Processing Units (GPUs) to accelerate test execution by running test cases […]

Graphs are a popular data structure to represent large amounts of data and the relationship between them. As serial hardware hits the wall in terms of computation speed, a lot of research has been made recently in parallelizing Graph Search Algorithms such as Breadth First Search or the Single Source Shortest Path Problem hence make […]

The DenseNet architecture is highly computationally efficient as a result of feature reuse. However, a naive DenseNet implementation can require a significant amount of GPU memory: If not properly managed, pre-activation batch normalization and contiguous convolution operations can produce feature maps that grow quadratically with network depth. In this technical report, we introduce strategies to […]

The conference will be held in Singapore during March 1-3, 2018. The theme of ICIAS2018 is “Frontier of intelligent autonomous systems”, reflecting the ever growing interests in research, development and applications in the dynamic and exciting areas of robotics. It also provides a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss […]

Publication All submissions will be peer reviewed 2-3 reviewers, and the accepted papers after registration will be published in the International Conference Proceedings Series by ACM, which will beindexed by Ei Compendex and Scopus. Submission ICRIS 2018 is now accepting manuscript submissions. Please submit your full paper to us: icris@academic.net

Deep learning algorithms base their success on building high learning capacity models with millions of parameters that are tuned in a data-driven fashion. These models are trained by processing millions of examples, so that the development of more accurate algorithms is usually limited by the throughput of the computing devices on which they are trained. […]