A survey on graph processing accelerators: Challenges and opportunities
Graph is a well known data structure to represent the associated relationships in a variety of
applications, eg, data science and machine learning. Despite a wealth of existing efforts on …
applications, eg, data science and machine learning. Despite a wealth of existing efforts on …
A modern primer on processing in memory
Modern computing systems are overwhelmingly designed to move data to computation. This
design choice goes directly against at least three key trends in computing that cause …
design choice goes directly against at least three key trends in computing that cause …
Processing data where it makes sense: Enabling in-memory computation
Today's systems are overwhelmingly designed to move data to computation. This design
choice goes directly against at least three key trends in systems that cause performance …
choice goes directly against at least three key trends in systems that cause performance …
Sisa: Set-centric instruction set architecture for graph mining on processing-in-memory systems
Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …
Non-relational databases on FPGAs: Survey, design decisions, challenges
Non-relational database systems (NRDS) such as graph and key-value have gained
attention in various trending business and analytical application domains. However, while …
attention in various trending business and analytical application domains. However, while …
Accelerating graph analytics on CPU-FPGA heterogeneous platform
S Zhou, VK Prasanna - 2017 29th International Symposium on …, 2017 - ieeexplore.ieee.org
Hardware accelerators for graph analytics have gained increasing interest. Vertex-centric
and edge-centric paradigms are widely used to design graph analytics accelerators …
and edge-centric paradigms are widely used to design graph analytics accelerators …
Understanding and improving the latency of DRAM-based memory systems
KK Chang - 2017 - search.proquest.com
Over the past two decades, the storage capacity and access bandwidth of main memory
have improved tremendously, by 128x and 20x, respectively. These improvements are …
have improved tremendously, by 128x and 20x, respectively. These improvements are …
Graph processing on fpgas: Taxonomy, survey, challenges
Graph processing has become an important part of various areas, such as machine
learning, computational sciences, medical applications, social network analysis, and many …
learning, computational sciences, medical applications, social network analysis, and many …
Boosting the performance of FPGA-based graph processor using hybrid memory cube: A case for breadth first search
Large graph processing has gained great attention in recent years due to its broad
applicability from machine learning to social science. Large real-world graphs, however, are …
applicability from machine learning to social science. Large real-world graphs, however, are …
Accelerating graph analytics by co-optimizing storage and access on an FPGA-HMC platform
Graph analytics, which explores the relationships among interconnected entities, is
becoming increasingly important due to its broad applicability, from machine learning to …
becoming increasingly important due to its broad applicability, from machine learning to …