Posts
Sep, 16
Out-of-core Implementation for Accelerator Kernels on Heterogeneous Clouds
Cloud environments today are increasingly featuring hybrid nodes containing multicore CPU processors and a diverse mix of accelerators such as Graphics Processing Units (GPUs), Intel Xeon Phi co-processors, and Field-Programmable Gate Arrays (FPGAs) to facilitate easier migration to them of HPC workloads. While virtualization of accelerators in clouds is a leading research challenge, we address […]
Sep, 16
Meta Networks for Neural Style Transfer
In this paper we propose a new method to get the specified network parameters through one time feed-forward propagation of the meta networks and explore the application to neural style transfer. Recent works on style transfer typically need to train image transformation networks for every new style, and the style is encoded in the network […]
Sep, 16
Empower Sequence Labeling with Task-Aware Neural Language Model
Linguistic sequence labeling is a general modeling approach that encompasses a variety of problems, such as part-of-speech tagging and named entity recognition. Recent advances in neural networks (NNs) make it possible to build reliable models without handcrafted features. However, in many cases, it is hard to obtain sufficient annotations to train these models. In this […]
Sep, 16
Monte Carlo methods for massively parallel computers
Applications that require substantial computational resources today cannot avoid the use of heavily parallel machines. Embracing the opportunities of parallel computing and especially the possibilities provided by a new generation of massively parallel accelerator devices such as GPUs, Intel’s Xeon Phi or even FPGAs enables applications and studies that are inaccessible to serial programs. Here […]
Sep, 16
End-to-end Deep Learning of Optimization Heuristics
Accurate automatic optimization heuristics are necessary for dealing with the complexity and diversity of modern hardware and software. Machine learning is a proven technique for learning such heuristics, but its success is bound by the quality of the features used. These features must be hand crafted by developers through a combination of expert domain knowledge […]
Sep, 12
Optimization of the Brillouin operator on the KNL architecture
Experiences with optimizing the matrix-times-vector application of the Brillouin operator on the Intel KNL processor are reported. Without adjustments to the memory layout, performance figures of 360 Gflop/s in single and 270 Gflop/s in double precision are observed. This is with N_c=3 colors, N_v=12 right-hand-sides, N_{thr}=256 threads, on lattices of size 32^3*64, using exclusively OMP […]
Sep, 12
GPU-Accelerated Parallel Finite-Difference Time-Domain Method for Electromagnetic Waves Propagation in Unmagnetized Plasma Media
The finite-difference time-domain (FDTD) method has been commonly utilized in the numerical solution of electromagnetic (EM) waves propagation through the plasma media. However, the FDTD method may bring about a significant increment in additional run-times consuming for computationally large and complicated EM problems. Graphics Processing Unit (GPU) computing based on Compute Unified Device Architecture (CUDA) […]
Sep, 12
Sorting with GPUs: A Survey
Sorting is a fundamental operation in computer science and is a bottleneck in many important fields. Sorting is critical to database applications, online search and indexing,biomedical computing, and many other applications. The explosive growth in computational power and availability of GPU coprocessors has allowed sort operations on GPUs to be done much faster than any […]
Sep, 12
Report: Performance comparison between C2075 and P100 GPU cards using cosmological correlation functions
In this report, some cosmological correlation functions are used to evaluate the differential performance between C2075 and P100 GPU cards. In the past, the correlation functions used in this work have been widely studied and exploited on some previous GPU architectures. The analysis of the performance indicates that a speedup in the range from 13 […]
Sep, 12
A Comparative Study of 2D Numerical Methods with GPU Computing
Graphics Processing Unit (GPU) computing is becoming an alternate computing platform for numerical simulations. However, it is not clear which numerical scheme will provide the highest computational efficiency for different types of problems. To this end, numerical accuracies and computational work of several numerical methods are compared using a GPU computing implementation. The Correction Procedure […]
Sep, 10
The 2nd International Conference on Machine Learning and Soft Computing (ICMLSC), 2018
ICMLSC 2018, The 2nd International Conference on Machine Learning and Soft Computing, will take place in Phu Quoc Island, Vietnam, from February 2-4, 2018. ICMLSC 2018 is co-organized by the University of Science, Vietnam and Industrial University of Ho Chi Minh City. ICMLSC 2018 is a not-to-be-missed opportunity that distills the most current knowledge on […]
Sep, 10
10th International Conference on Computer and Automation Engineering (ICCAE), 2018
After the successes of ICCAE 2009 (Bangkok, Thailand), ICCAE 2010 (Singapore), ICCAE 2011 (Chongqing, China), ICCAE 2012 (Mumbai, India), ICCAE 2013 (Bruxelles, Belgium), ICCAE 2014 (Melbourne, Australia), ICCAE 2015 (Bali, Indonesia), ICCAE 2016 (Melbourne, Australia), ICCAE 2017 (Sydney, Australia), 2018 10th International Conference on Computer and Automation Engineering (ICCAE 2018) is going to take place […]