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Posts

Jul, 5

A Fast Method For Computing Principal Curvatures From Range Images

Estimation of surface curvature from range data is important for a range of tasks in computer vision and robotics, object segmentation, object recognition and robotic grasping estimation. This work presents a fast method of robustly computing accurate metric principal curvature values from noisy point clouds which was implemented on GPU. In contrast to existing readily […]
Jul, 5

A Linear Algebra Approach to Fast DNA Mixture Analysis Using GPUs

Analysis of DNA samples is an important step in forensics, and the speed of analysis can impact investigations. Comparison of DNA sequences is based on the analysis of short tandem repeats (STRs), which are short DNA sequences of 2-5 base pairs. Current forensics approaches use 20 STR loci for analysis. The use of single nucleotide […]
Jul, 5

Parle: parallelizing stochastic gradient descent

We propose a new algorithm called Parle for parallel training of deep networks that converges 2-4x faster than a data-parallel implementation of SGD, while achieving significantly improved error rates that are nearly state-of-the-art on several benchmarks including CIFAR-10 and CIFAR-100, without introducing any additional hyper-parameters. We exploit the phenomenon of flat minima that has been […]
Jul, 2

GPU-acceleration for Large-scale Tree Boosting

In this paper, we present a novel massively parallel algorithm for accelerating the decision tree building procedure on GPUs (Graphics Processing Units), which is a crucial step in Gradient Boosted Decision Tree (GBDT) and random forests training. Previous GPU based tree building algorithms are based on parallel multi-scan or radix sort to find the exact […]
Jul, 2

Speeding up lattice sieve with Xeon Phi coprocessor

Major substep in a lattice sieve algorithm which solves the Euclidean shortest vector problem (SVP) is the computation of sums and Euclidean norms of many vector pairs. Finding a solution to the SVP is the foundation of an attack against many lattice based crypto systems. We optimize the main subfunction of a sieve for the […]
Jul, 2

Snowflake: A Lightweight Portable Stencil DSL

Stencil computations are not well optimized by general-purpose production compilers and the increased use of multicore, manycore, and accelerator-based systems makes the optimization problem even more challenging. In this paper we present Snowflake, a Domain Specific Language (DSL) for stencils that uses a "micro-compiler" approach, i.e., small, focused, domain-specific code generators. The approach is similar […]
Jul, 2

Deep neural networks for direct, featureless learning through observation: the case of 2d spin models

We train a deep convolutional neural network to accurately predict the energies and magnetizations of Ising model configurations, using both the traditional nearest-neighbour Hamiltonian, as well as a long-range screened Coulomb Hamiltonian. We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbour energy of the 4×4 Ising model. Using its success […]
Jul, 2

Synthesis of Embedded Software using Dataflow Schedule Graphs

In the design and implementation of digital signal processing (DSP) systems, dataflow is recognized as a natural model for specifying applications, and dataflow enables useful model-based methodologies for analysis, synthesis, and optimization of implementations. A wide range of embedded signal processing applications can be designed efficiently using the high level abstractions that are provided by […]
Jun, 25

DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications

The rapid emergence of head-mounted devices such as the Microsoft Holo-lens enables a wide variety of continuous vision applications. Such applications often adopt deep-learning algorithms such as CNN and RNN to extract rich contextual information from the first-person-view video streams. Despite the high accuracy, use of deep learning algorithms in mobile devices raises critical challenges, […]
Jun, 25

Scalar collapse in AdS with an OpenCL open source code

We study the spherically symmetric collapse of a scalar field in anti-de Sitter spacetime using a newly constructed, open-source code which parallelizes over heterogeneous architectures using the open standard OpenCL. An open question for this scenario concerns how to tell, a priori, whether some form of initial data will be stable or will instead develop […]
Jun, 25

ART vs. NDK vs. GPU acceleration: A study of performance of image processing algorithms on Android

The Android ecosystem contains three major platforms for execution suitable for different purposes. Android applications are normally written in the Java programming language, but computationally intensive parts of Android applications can be sped up by choosing to use a native language or by utilising the parallel architecture found in graphics processing units (GPUs). The experiments […]
Jun, 25

An Analysis of Variation Between Cores For Intel Xeon Phi Knights Corner And Xeon Phi Knights Landing

As we move towards exascale computing, the efficiency of application performance and energy utilization, must be optimized by redefining architectural features and application performance analysis. This research analyzes the performance per core of 8 applications on Intel Xeon Phi Knights Corner (KNC) and Knights Landing (KNL) to determine if performance variation within cores can lead […]
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