17584

Posts

Sep, 21

Asynchronous Task-Based Polar Decomposition on Single Node Manycore Architectures

This paper introduces the first asynchronous, task-based formulation of the polar decomposition and its corresponding implementation on manycore architectures. Based on a new formulation of the iterative QR dynamically-weighted Halley algorithm (QDWH) for the calculation of the polar decomposition, the proposed implementation replaces the original and hostile LU factorization for the condition number estimator by […]
Sep, 21

Accelerating Radio Astronomy with Auto-Tuning

The goal of this thesis is to show a way to improve the performance of different radio astronomy applications. To begin with, in this thesis we advocate the use of many-core accelerators, parallel processors with hundreds of computational cores, as execution platforms for widely used radio astronomy algorithms and platforms. However, we also show that […]
Sep, 21

IBM Deep Learning Service

Deep learning driven by large neural network models is overtaking traditional machine learning methods for understanding unstructured and perceptual data domains such as speech, text, and vision. At the same time, the "as-a-Service"-based business model on the cloud is fundamentally transforming the information technology industry. These two trends: deep learning, and "as-a-service" are colliding to […]
Sep, 21

Automated Testing of Graphics Shader Compilers

We present an automated technique for finding defects in compilers for graphics shading languages. A key challenge in compiler testing is the lack of an oracle that classifies an output as correct or incorrect; this is particularly pertinent in graphics shader compilers where the output is a rendered image that is typically under-specified. Our method […]
Sep, 21

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks […]
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

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

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

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) […]
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