Mar, 22

Proteus: Efficient Resource Use in Heterogeneous Architectures

Current processors provide a variety of different processing units to improve performance and power efficiency. For example, ARM’S big.LITTLE, AMD’s APUs, and Oracle’s M7 provide heterogeneous processors, on-die GPUs, and ondie accelerators. However, the performance experienced by programs on these accelerators can be highly variable due to issues like contention from multiprogramming or thermal constraints. […]
Mar, 22

Recurrent neural networks for language modeling

The goal of the thesis is to explore the mechanisms and tools that enables efficient development of Recurrent Neural Networks, how to train them and what they can accomplish in regard to character level language modelling. Specifically Gated Recurrence Units and Long Short Term Memory are the focal point of the training and language modelling. […]
Mar, 22

A Survey of Techniques for Architecting and Managing GPU Register File

To support their massively-multithreaded architecture, GPUs use very large register file (RF) which has a capacity higher than even L1 and L2 caches. In total contrast, traditional CPUs use tiny RF and much larger caches to optimize latency. Due to these differences, along with the crucial impact of RF in determining GPU performance, novel and […]
Mar, 20

OpenCL Cryptographic Library

Modern GPUs are devices with very high parallelism for a very low cost. Integer and logic instruction support enable us to use them for many workloads unrelated to rendering. Cryptographic algorithms like AES or Blowfish can benefit from being executed on the system’s GPU. Such execution off-loads work from the main CPU, freeing it to […]
Mar, 20

Acceleration of ensemble machine learning methods using many-core devices

We present a case study into the acceleration of ensemble machine learning methods using many-core devices in collaboration with Toshiba Medical Visualisation Systems Europe (TMVSE). The adoption of GPUs to execute a key algorithm in the classification of medical image data was shown to significantly reduce overall processing time. Using a representative dataset and pre-trained […]
Mar, 20

A Massively Parallel Algorithm for Cell Classification Using CUDA

In Bioinformatics, cell classification is the act of separating human cells into different groups based on their RNA-seq expression levels. These data can be quite large, as there are about 20,000 known human genes. Even relatively small datasets (< 1000 cell samples) can contains millions of values. Computations and classifications on this data force a […]
Mar, 20

Automatic Detection and Denoising of Signals in Large Geophysical Datasets

To fully understand the complex interactions of various phenomena in the natural world, scientific disciplines such as geology and seismology increasingly rely upon analyzing large amounts of observations. However, data collection is growing at a faster rate than what is currently possible to analyze through traditional approaches. These datasets, supplied by the increasing use of […]
Mar, 20

Analyzing and Improving the Performance of Spatial Database Processing

Spatial databases have become increasingly important, due to the advent of popular geospatial Web services such as Google Maps, GPS navigation systems, and a host of accompanying location-based services. Spatial databases are used in a variety of real-world applications involving complex data analytics: land surveying, urban planning, environmental assessments, or new BigData application domains like […]
Mar, 18

4th International Workshop on OpenCL (IWOCL), 2016

There is a great program lined up for IWOCL 2016 in Vienna this April 19-21: IWOCL 2016 Sessions The 10% early bird registration discount ends March 20th, so don’t delay, register today!
Mar, 15

DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices

Breakthroughs from the field of deep learning are radically changing how sensor data are interpreted to extract the high-level information needed by mobile apps. It is critical that the gains in inference accuracy that deep models afford become embedded in future generations of mobile apps. In this work, we present the design and implementation of […]
Mar, 15

DySel: Lightweight Dynamic Selection for Kernel-based Data-parallel Programming Model

The rising pressure for simultaneously improving performance and reducing power is driving more diversity into all aspects of computing devices. An algorithm that is wellmatched to the target hardware can run multiple times faster and more energy efficiently than one that is not. The problem is complicated by the fact that a program’s input also […]
Mar, 15

Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code

The current trend in next-generation exascale systems goes towards integrating a wide range of specialized (co-)processors into traditional supercomputers. However, the integration of different specialized devices increases the degree of heterogeneity and the complexity in programming such type of systems. Due to the efficiency of heterogeneous systems in terms of Watt and FLOPS per surface […]
Page 50 of 909« First...102030...4849505152...607080...Last »

Recent source codes

* * *

* * *

TwitterAPIExchange Object
    [oauth_access_token:TwitterAPIExchange:private] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
    [oauth_access_token_secret:TwitterAPIExchange:private] => o29ji3VLVmB6jASMqY8G7QZDCrdFmoTvCDNNUlb7s
    [consumer_key:TwitterAPIExchange:private] => TdQb63pho0ak9VevwMWpEgXAE
    [consumer_secret:TwitterAPIExchange:private] => Uq4rWz7nUnH1y6ab6uQ9xMk0KLcDrmckneEMdlq6G5E0jlQCFx
    [postfields:TwitterAPIExchange:private] => 
    [getfield:TwitterAPIExchange:private] => ?cursor=-1&screen_name=hgpu&skip_status=true&include_user_entities=false
    [oauth:protected] => Array
            [oauth_consumer_key] => TdQb63pho0ak9VevwMWpEgXAE
            [oauth_nonce] => 1487819611
            [oauth_signature_method] => HMAC-SHA1
            [oauth_token] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
            [oauth_timestamp] => 1487819611
            [oauth_version] => 1.0
            [cursor] => -1
            [screen_name] => hgpu
            [skip_status] => true
            [include_user_entities] => false
            [oauth_signature] => VPDFVbT/1HrY6rmMBtJ0oX07pAo=

    [url] => https://api.twitter.com/1.1/users/show.json
Follow us on Facebook
Follow us on Twitter

HGPU group

2173 peoples are following HGPU @twitter

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: