Concept

Granularity (parallel computing)

Related publications (51)

Task scheduling for highly concurrent analytical and transaction workloads

Systems and method for a task scheduler with dynamic adjustment of concurrency levels and task granularity are disclosed for improved execution of highly concurrent analytical and transactional systems. The task scheduler can avoid both over commitment and ...

2020

Fully-Asynchronous Fully-Implicit Variable-Order Variable-Timestep Simulation of Neural Networks

Felix Schürmann, Michael Lee Hines, Bruno Ricardo Da Cunha Magalhães

State-of-the-art simulations of detailed neurons follow the Bulk Synchronous Parallel execution model. Execution is divided in equidistant communication intervals, with parallel neurons interpolation and collective communication guiding synchronization. Su ...

2020

On Linear Learning with Manycore Processors

Martin Jaggi

A new generation of manycore processors is on the rise that offers dozens and more cores on a chip and, in a sense, fuses host processor and accelerator. In this paper we target the efficient training of generalized linear models on these machines. We prop ...

IEEE COMPUTER SOC2019

HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines

Anastasia Ailamaki, Periklis Chrysogelos, Manolis Karpathiotakis, Raja Appuswamy

Modern server hardware is increasingly heterogeneous as hardware accelerators, such as GPUs, are used together with multicore CPUs to meet the computational demands of modern data analytics workloads. Unfortunately, query parallelization techniques used by ...

2019

Asynchronous Branch-Paralle Simulation of Detailed Neuron Models

Michael Lee Hines

Simulations of electrical activity of networks of morphologically detailed neuron models allow for a better understanding of the brain. State-of-the-art simulations describe the dynamics of ionic currents and biochemical processes within branching topologi ...

FRONTIERS MEDIA SA2019

An Associativity-Agnostic in-Cache Computing Architecture Optimized for Multiplication

David Atienza Alonso, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, Marco Antonio Rios, William Andrew Simon

With the spread of cloud services and Internet of Things concept, there is a popularization of machine learning and artificial intelligence based analytics in our everyday life. However, an efficient deployment of these data-intensive services requires per ...

2019

Stop Crying Over Your Cache Miss Rate: Handling Efficiently Thousands of Outstanding Misses in FPGAs

Paolo Ienne, Mikhail Asiatici

FPGAs rely on massive datapath parallelism to accelerate applications even with a low clock frequency. However, applications such as sparse linear algebra and graph analytics have their throughput limited by irregular accesses to external memory for which ...

ASSOC COMPUTING MACHINERY2019

Hardware Acceleration of HDR-Image Tone Mapping on an FPGA-CPU Platform Through High-Level Synthesis

Christian Enz, Vincent Frédéric Camus, Alessandro Pezzotta, Jérémy Lucien Maurice Schlachter, Mattia Cacciotti

In this paper, the hardware acceleration of a tone-mapping algorithm for High-Dynamic-Range image processing is presented. Starting from the C++ source code, High-Level Synthesis has been performed using Xilinx SDSoC for a Xilinx Zynq SoC device. After an ...

IEEE2018

Scaling and Resilience in Numerical Algorithms for Exascale Computing

Allan Svejstrup Nielsen

The first Petascale supercomputer, the IBM Roadrunner, went online in 2008. Ten years later, the community is now looking ahead to a new generation of Exascale machines. During the decade that has passed, several hundred Petascale capable machines have bee ...

EPFL2018

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

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.