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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 …
Cloud computing landscape and research challenges regarding trust and reputation
Cloud Computing is an emerging computing paradigm. It shares massively scalable, elastic
resources (eg, data, calculations, and services) transparently among the users over a …
resources (eg, data, calculations, and services) transparently among the users over a …
DAWG: A defense against cache timing attacks in speculative execution processors
Software side channel attacks have become a serious concern with the recent rash of
attacks on speculative processor architectures. Most attacks that have been demonstrated …
attacks on speculative processor architectures. Most attacks that have been demonstrated …
Fusion++: Volumetric object-level slam
We propose an online object-level SLAM system which builds a persistent and accurate 3D
graph map of arbitrary reconstructed objects. As an RGB-D camera browses a cluttered …
graph map of arbitrary reconstructed objects. As an RGB-D camera browses a cluttered …
Hemem: Scalable tiered memory management for big data applications and real nvm
High-capacity non-volatile memory (NVM) is a new main memory tier. Tiered DRAM+ NVM
servers increase total memory capacity by up to 8x, but can diminish memory bandwidth by …
servers increase total memory capacity by up to 8x, but can diminish memory bandwidth by …
The GAP benchmark suite
S Beamer, K Asanović, D Patterson - ar** to standardize
graph processing evaluations. Fewer differences between graph processing evaluations will …
graph processing evaluations. Fewer differences between graph processing evaluations will …
Graphit: A high-performance graph dsl
The performance bottlenecks of graph applications depend not only on the algorithm and
the underlying hardware, but also on the size and structure of the input graph. As a result …
the underlying hardware, but also on the size and structure of the input graph. As a result …
SpaceA: Sparse matrix vector multiplication on processing-in-memory accelerator
Sparse matrix-vector multiplication (SpMV) is an important primitive across a wide range of
application domains such as scientific computing and graph analytics. Due to its intrinsic …
application domains such as scientific computing and graph analytics. Due to its intrinsic …
Exploiting locality in graph analytics through hardware-accelerated traversal scheduling
Graph processing is increasingly bottlenecked by main memory accesses. On-chip caches
are of little help because the irregular structure of graphs causes seemingly random memory …
are of little help because the irregular structure of graphs causes seemingly random memory …
Xor filters: Faster and smaller than bloom and cuckoo filters
The Bloom filter provides fast approximate set membership while using little memory.
Engineers often use these filters to avoid slow operations such as disk or network accesses …
Engineers often use these filters to avoid slow operations such as disk or network accesses …