15938

Posts

May, 30

Deep API Learning

Developers often wonder how to implement a certain functionality (e.g., how to parse XML files) using APIs. Obtaining an API usage sequence based on an API-related natural language query is very helpful in this regard. Given a query, existing approaches utilize information retrieval models to search for matching API sequences. These approaches treat queries and […]
May, 28

Data Remanence and Digital Forensic Investigation for CUDA Graphics Processing Units

This paper investigates the practicality of memory attacks on commercial Graphics Processing Units (GPUs). With recent advances in the performance and viability of using GPUs for various highly-parallelised data processing tasks, a number of security challenges are raised. Unscrupulous software running subsequently on the same GPU, either by the same user, or another user, in […]
May, 28

Efficient High-Speed WPA2 Brute Force Attacks using Scalable Low-Cost FPGA Clustering

WPA2-Personal is widely used to protect Wi-Fi networks against illicit access. While attackers typically use GPUs to speed up the discovery of weak network passwords, attacking random passwords is considered to quickly become infeasible with increasing password length. Professional attackers may thus turn to commercial high-end FPGA-based cluster solutions to significantly increase the speed of […]
May, 28

An OpenMP Programming Environment on Mobile Devices

Recently, the computational speed and battery capability of mobile devices were greatly prompted. With an enormous number of APPs, users can do many things in mobile devices as well as in computers. Consequently, more and more scientific researchers are encouraged to move their working environment from computers to mobile devices for increasing their work efficiency […]
May, 28

EPEM: A General and Validated Energy Complexity Model for Multithreaded Algorithms

Like time complexity models that have significantly contributed to the analysis and development of fast algorithms, energy complexity models for parallel algorithms are desired as crucial means to develop energy efficient algorithms for ubiquitous multicore platforms. Ideal energy complexity models should be validated on real multicore platforms and applicable to a wide range of parallel […]
May, 28

Multi-threaded Geant4 on the Xeon-Phi with Complex High-Energy Physics Geometry

To study the performance of multi-threaded Geant4 for high-energy physics experiments, an application has been developed which generalizes and extends previous work. A highly-complex detector geometry is used for benchmarking on an Intel Xeon Phi coprocessor. In addition, an implementation of parallel I/O based on Intel SCIF and ROOT technologies is incorporated and studied.
May, 28

Theano-MPI: a Theano-based Distributed Training Framework

We develop a scalable and extendable training framework that can utilize GPUs across nodes in a cluster and accelerate the training of deep learning models based on data parallelism. Both synchronous and asynchronous training are implemented in our framework, where parameter exchange among GPUs is based on CUDA-aware MPI. In this report, we analyze the […]
May, 26

Faster GPU-based convolutional gridding via thread coarsening

Convolutional gridding is a processor-intensive step in interferometric imaging. While it is possible to use graphics processing units (GPUs) to accelerate this operation, existing methods use only a fraction of the available flops. We apply thread coarsening to improve the efficiency of an existing algorithm, and observe performance gains of up to 3.2x for single-polarization […]
May, 26

Implementing Deep Neural Networks for Financial Market Prediction on the Intel Xeon Phi

Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable attention in the speech transcription and image recognition community (Krizhevsky et al., 2012) for their superior predictive properties including robustness to overfitting. However their application to financial market prediction has not been previously […]
May, 26

Learning a Metric Embedding for Face Recognition using the Multibatch Method

This work is motivated by the engineering task of achieving a near state-of-the-art face recognition on a minimal computing budget running on an embedded system. Our main technical contribution centers around a novel training method, called Multibatch, for similarity learning, i.e., for the task of generating an invariant "face signature" through training pairs of "same" […]
May, 26

Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU

In this paper, we present that security threats coming with existing GPU memory management strategy are overlooked, which opens a back door for adversaries to freely break the memory isolation: they enable adversaries without any privilege in a computer to recover the raw memory data left by previous processes directly. More importantly, such attacks can […]
May, 26

PROJECTION Algorithm for Motif Finding on GPUs

Motif finding is one of the NP-complete problems in Computational Biology. Existing nondeterministic algorithms for motif finding do not guarantee the global optimality of results and are sensitive to initial parameters. To address this problem, the PROJECTION algorithm provides a good initial estimate that can be further refined using local optimization algorithms such as EM, […]
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