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Posts

Nov, 17

word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement

Deep learning natural language processing models often use vector word embeddings, such as word2vec or GloVe, to represent words. A discrete sequence of words can be much more easily integrated with downstream neural layers if it is represented as a sequence of continuous vectors. Also, semantic relationships between words, learned from a text corpus, can […]
Nov, 17

Deep Learning Based FPGA-CPU Acceleration

The purpose of this project is to continue exploring new ways of accelerating sequential computer code, and finding out if the machine learning techniques available today are able to help us in this task. The core idea is trying to parallelize during run-time (in a way completely transparent to the programmer) the code that’s being […]
Nov, 17

A Highly Parameterizable Framework for Conditional Restricted Boltzmann Machine Based Workloads Accelerated With FPGAs and OpenCL

Conditional Restricted Boltzmann Machine (CRBM) is a promising candidate for a multidimensional system modeling that can learn a probability distribution over a set of data. It is a specific type of an artificial neural network with one input (visible) and one output (hidden) layer. Recently published works demonstrate that CRBM is a suitable mechanism for […]
Nov, 10

Framework for Parallel Kernels Auto-tuning

The result of this thesis is a framework for auto-tuning of parallel kernels which are written in either OpenCL or CUDA language. The framework includes advanced functionality such as support for composite kernels and online auto-tuning. The thesis describes API and internal structure of the framework and presents several examples of its utilization for kernel […]
Nov, 10

Study of OpenCL Processing Models for FPGA Devices

In our study, we present the results of the implementation of the SHA-512 algorithm in FPGAs. The distinguished element of our work is that we conducted the work using OpenCL for FPGA, which is a relatively new development method for reconfigurable logic. We examine loop unrolling as an OpenCL performance optimization method and compare the […]
Nov, 10

CL-VIS: Visualization Platform for Understanding and Checking the OpenCL Programs

Due to GPU’s improved hardware performance, many researchers have tried to utilize the GPU for computer vision, image processing, cryptography, and artificial intelligence. As results, the GPU could successfully speed up algorithms from tens to hundreds of times in many cases. However, GPU programming is still known to be difficult because of its different characteristics […]
Nov, 10

KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs

In this paper we present KLARAPTOR (Kernel LAunch parameters RAtional Program estimaTOR), a new tool built on top of the LLVM Pass Framework and NVIDIA CUPTI API to dynamically determine the optimal values of kernel launch parameters of a CUDA program P. To be precise, we describe a novel technique to statically build (at the […]
Nov, 10

Accelerating Stochastic Simulations on GPUs Using OpenCL

Since first introduced in 2008 with the 1.0 specification, OpenCL has steadily evolved over the decade to increase its support for heterogeneous parallel systems. In this paper, we accelerate stochastic simulation of biochemical reaction networks on modern GPUs (graphics processing units) by means of the OpenCL programming language. In implementing the OpenCL version of the […]
Nov, 9

8th International Workshop on OpenCL, including SYCLCon, 2019

Join us at the 8th International Workshop on OpenCL, including SYCLcon 2020, for three days of talks, workshops and community networking aimed at furthering the collaboration and knowledge sharing amongst the international community of high-performance computing specialist working with OpenCL, SYCL, SPIR and Vulkan Compute. The event provides a rich mix of hands-on tutorials, technical […]
Nov, 3

JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training

In 2019, around 57% of the population of the world has broadband access to the Internet. Moreover, there are 5.9 billion mobile broadband subscriptions, i.e., 1.3 subscriptions per user. So there is an enormous interconnected computational power held by users all around the world. Also, it is estimated that Internet users spend more than six […]
Nov, 3

Code Optimization on GPUs

Graphic Processing Units (GPUs) have become popular in the last decade due to their high memory bandwidth and powerful computing capacity. Nevertheless, achieving highperformance on GPUs is not trivial. It generally requires significant programming expertise and understanding of details of low-level execution mechanisms in GPUs. This dissertation introduces approaches for optimizing regular and irregular applications. […]
Nov, 3

In-memory database acceleration on FPGAs: a survey

While FPGAs have seen prior use in database systems, in recent years interest in using FPGA to accelerate databases has declined in both industry and academia for the following three reasons. First, specifically for in-memory databases, FPGAs integrated with conventional I/O provide insufficient bandwidth, limiting performance. Second, GPUs, which can also provide high throughput, and […]

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