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Large-Scale Graph Processing on FPGAs with Caches for Thousands of Simultaneous Misses

Related publications (40)

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Stationary signal processing on graphs

Pierre Vandergheynst, Nathanaël Perraudin

Graphs are a central tool in machine learning and information processing as they allow to conveniently capture the structure of complex datasets. In this context, it is of high importance to develop flexible models of signals defined over graphs or network ...

Institute of Electrical and Electronics Engineers2017

Assembling a Network out of Ambiguous Patches

Matthias Grossglauser, Lyudmila Yartseva, Jefferson Elbert Simões

Many graph mining and network analysis problems rely on the availability of the full network over a set of nodes. But inferring a full network is sometimes non-trivial if the raw data is in the form of many small {\em patches} or subgraphs, of the true net ...

2016

Multi-Library Coded Caching

Michael Christoph Gastpar, Saeid Sahraei

We study the problem of coded caching when the server has access to several libraries and each user makes independent requests from every library. The single-library scenario has been well studied and it has been proved that coded caching can significantly ...

2016

Exploiting NVM in Large-scale Graph Analytics

Willy Zwaenepoel, Jasmina Malicevic

Data center applications like graph analytics require servers with ever larger memory capacities. DRAM scaling, how- ever, is not able to match the increasing demands for ca- pacity. Emerging byte-addressable, non-volatile memory technologies (NVM) offer a ...

2015

Multi-Gigabyte On-Chip DRAM Caches for Servers

Dorde Jevdic

While DRAM latency has long been recognized as a major bottleneck in servers, DRAM bandwidth is emerging as an important bottleneck as server processors shift to many-core architectures to allow for sustainable throughput improvements. The rapid expansion ...

EPFL2015

A Primer on Hardware Prefetching

Babak Falsafi

Since the 1970’s, microprocessor-based digital platforms have been riding Moore’s law, allowing for doubling of density for the same area roughly every two years. However, whereas microprocessor fabrication has focused on increasing instruction execution r ...

Morgan & Claypool2014

A Bayesian Method for Matching Two Similar Graphs without Seeds

Matthias Grossglauser, Pedram Pedarsani

Approximate graph matching (AGM) refers to the problem of mapping the vertices of two structurally similar graphs, which has applications in social networks, computer vision, chemistry, and biology. Given its computational cost, AGM has mostly been limited ...

2013

X-Stream: Edge-centric Graph Processing using Streaming Partitions

Willy Zwaenepoel, Amitabha Roy, Ivo Mihailovic

X-Stream is a system for processing both in-memory and out-of-core graphs on a single shared-memory machine. While retaining the scatter-gather programming model with state stored in the vertices, X-Stream is novel in (i) using an edge-centric rather than ...

ACM2013

Replica Bit-Line Technique for Embedded Multilevel Gain-Cell DRAM

Andreas Peter Burg, Pascal Andreas Meinerzhagen, Muhammad Umer Khalid

Multilevel gain-cell DRAMs are interesting to improve the area-efficiency of modern fault-tolerant systems-on-chip implemented in deep-submicron CMOS technologies. This paper addresses the problem of long access times in such multilevel gain-cell DRAMs, wh ...

Ieee2012

Architectural Support for Coherent Architecturally Visible Storage in Instruction Set Extensions

Ties Jan Henderikus Kluter

When it comes to performance, embedded systems share many problems with their higher-end counterparts. The growing gap between top processor frequency and memory access speed, the memory wall, is one such problem. Driven, in part, by low energy consumption ...

EPFL2010