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Posts

Feb, 10

Development of JavaScript-based deep learning platform and application to distributed training

Deep learning is increasingly attracting attention for processing big data. Existing frameworks for deep learning must be set up to specialized computer systems. Gaining sufficient computing resources therefore entails high costs of deployment and maintenance. In this work, we implement a matrix library and deep learning framework that uses JavaScript. It can run on web […]
Feb, 10

gearshifft – The FFT Benchmark Suite for Heterogeneous Platforms

Fast Fourier Transforms (FFTs) are exploited in a wide variety of fields ranging from computer science to natural sciences and engineering. With the rising data production bandwidths of modern FFT applications, judging best which algorithmic tool to apply, can be vital to any scientific endeavor. As tailored FFT implementations exist for an ever increasing variety […]
Feb, 10

GPU-Accelerated SVM Training Algorithm Based on PC and Mobile Device

This work is to design an accelerated SVM (Support Vector Machine) which is suitable for Android operating system. SVM is widely used in the health-related applications. The SVM provides a potential classification technology based on the pattern recognition method and statistical learning theory. This paper proposes a parallel SVM algorithm based on GPU accelerator. GPU […]
Feb, 10

Acceleration of low-latency gravitational wave searches using Maxwell-microarchitecture GPUs

Low-latency detections of gravitational waves (GWs) are crucial to enable prompt follow-up observations to astrophysical transients by conventional telescopes. We have developed a low-latency pipeline using a technique called Summed Parallel Infinite Impulse Response (SPIIR) filtering, realized by a Graphic Processing Unit (GPU). In this paper, we exploit the new Maxwell memory access architecture in […]
Feb, 10

Backpropagation Training for Fisher Vectors within Neural Networks

Fisher-Vectors (FV) encode higher-order statistics of a set of multiple local descriptors like SIFT features. They already show good performance in combination with shallow learning architectures on visual recognitions tasks. Current methods using FV as a feature descriptor in deep architectures assume that all original input features are static. We propose a framework to jointly […]
Feb, 7

CFP: The 2017 International Workshop on Embedded Multicore Systems (ICPPEMS), 2017

The 2017 International Workshop on Embedded Multicore Systems to be held in conjunction with the 46th International Conference on Parallel Processing (ICPP 2017) https://sites.google.com/view/icppems2017 Embedded systems with multicore designs are of major focuses from both industry and academia. While embedded multicore systems will look to play an important role ahead for system designs, many challenging […]
Feb, 7

Machines and Algorithms

I discuss the evolution of computer architectures with a focus on QCD and with reference to the interplay between architecture, engineering, data motion and algorithms. New architectures are discussed and recent performance results are displayed. I also review recent progress in multilevel solver and integation algorithms.
Feb, 7

Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets

This paper presents a model based on Deep Learning algorithms of LSTM and GRU for facilitating an anomaly detection in Large Hadron Collider superconducting magnets. We used high resolution data available in Post Mortem database to train a set of models and chose the best possible set of their hyper-parameters. Using Deep Learning approach allowed […]
Feb, 7

Introduction to the Special Issue on Digital Signal Processing in Radio Astronomy

Advances in astronomy are intimately linked to advances in digital signal processing (DSP). This special issue is focused upon advances in DSP within radio astronomy. The trend within that community is to use off-the-shelf digital hardware where possible and leverage advances in high performance computing. In particular, graphics processing units (GPUs) and field programmable gate […]
Feb, 7

A Dynamic Programming Model To Solve Optimisation Problems Using GPUs

This thesis presents a parallel, dynamic programming based model which is deployed on the GPU of a system to accelerate the solving of optimisation problems. This is achieved by simultaneously running GPU based computations, and memory transactions, allowing computation to never pause, and overcoming the memory constraints of solving large problem instances. Due to this […]
Feb, 7

Advanced Concurrency Control Algorithm Design and GPU System Support for High Performance In-Memory Data Management

The design and implementation of data management systems have been significantly affected by application demands and hardware advancements. On one hand, with the emerging of various new applications, the traditional one-size-fits-all data management system has evolved into domain specific systems optimized for each application (e.g., OLTP, OLAP, streaming, etc.). On the other hand, with increasing […]
Feb, 5

Critical Comparison of the Classification Ability of Deep Convolutional Neural Network Frameworks with Support Vector Machine Techniques in the Image Classification Process

Recently, a number of new image classification models have been developed to diversify the number of options available to prospective machine learning classifiers, such as Deep Learning. This is particularly important in the field of medical image classification as a misdiagnosis could have a severe impact on the patient. However, an assessment on the level […]
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