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Comparing multimedia storage architectures

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Distributed Learning of CNNs on Heterogeneous CPU/GPU Architectures

José Pedro Rebelo Ferreira Marques, Gabriel Falcao Paiva Fernandes

The convolutional neural networks (CNNs) have proven to be powerful classification tools in tasks that range from check reading to medical diagnosis, reaching close to human perception, and in some cases surpassing it. However, the problems to solve are be ...

TAYLOR & FRANCIS INC2018

Massively parallel data processing for quantitative total flow imaging with optical coherence microscopy and tomography

Theo Lasser, Paul James Marchand, Daniel Pawel Szlag, Marcin Antoni Sylwestrzak

We present an application of massively parallel processing of quantitative flow measurements data acquired using spectral optical coherence microscopy (SOCM). The need for massive signal processing of these particular datasets has been a major hurdle for m ...

Elsevier2017

Memory and logic integration on the same chip is becoming increasingly cost effective, creating the opportunity to offload data-intensive functionality to processing units placed inside memory chips. The introduction of memory-side processing units (MPUs) ...

Ieee2017

A GPU-Friendly Skiplist Algorithm

Nachshon Cohen

We propose a design for a fine-grained lock-based skiplist optimized for Graphics Processing Units (GPUs). While GPUs are often used to accelerate streaming parallel computations, it remains a significant challenge to efficiently offload concurrent computa ...

Ieee2017

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In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from m ...

Iop Publishing Ltd2016

Execution Trace Graph Based Multi-criteria Partitioning of Stream Programs

Marco Mattavelli, Simone Casale Brunet, Endri Bezati, Malgorzata Maria Michalska

One of the problems proven to be NP-hard in the field of many-core architectures is the partitioning of stream programs. In order to maximize the execution parallelism and obtain the maximal data throughput for a streaming application it is essential to fi ...

2015

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The dramatic rise of time-series data in a variety of contexts, such as social networks, mobile sensing, data centre monitoring, etc., has fuelled interest in obtaining real-time insights from such data using distributed stream processing systems. One such ...

2015

Cofactorization on Graphics Processing Units

Arjen Lenstra, Joppe Willem Bos, Thorsten Kleinjung, Andrea Miele

We show how the cofactorization step, a compute-intensive part of the relation collection phase of the number field sieve (NFS), can be farmed out to a graphics processing unit. Our implementation on a GTX 580 GPU, which is integrated with a state-of-the-a ...

Springer-Verlag Berlin2014

Low Power and Scalable Many-Core Architecture for Big-Data Stream Computing

David Atienza Alonso, Karim Kanoun, Martino Ruggiero

In the last years the process of examining large amounts of different types of data, or Big-Data, in an effort to uncover hidden patterns or unknown correlations has become a major need in our society. In this context, stream mining applications are now wi ...

IEEE Press2014

Today, managing, storing and analyzing data continuously in order to gain additional insight is becoming commonplace. Data analytics engines have been traditionally optimized for read-only queries assuming that the main data reside on mechanical disks. The ...

EPFL2014