Thinking like a vertex: A survey of vertex-centric frameworks for large-scale distributed graph processing
The vertex-centric programming model is an established computational paradigm recently
incorporated into distributed processing frameworks to address challenges in large-scale …
incorporated into distributed processing frameworks to address challenges in large-scale …
Trends in big data analytics
One of the major applications of future generation parallel and distributed systems is in big-
data analytics. Data repositories for such applications currently exceed exabytes and are …
data analytics. Data repositories for such applications currently exceed exabytes and are …
Gunrock: A high-performance graph processing library on the GPU
For large-scale graph analytics on the GPU, the irregularity of data access/control flow and
the complexity of programming GPUs have been two significant challenges for developing a …
the complexity of programming GPUs have been two significant challenges for developing a …
Ligra: a lightweight graph processing framework for shared memory
J Shun, GE Blelloch - Proceedings of the 18th ACM SIGPLAN …, 2013 - dl.acm.org
There has been significant recent interest in parallel frameworks for processing graphs due
to their applicability in studying social networks, the Web graph, networks in biology, and …
to their applicability in studying social networks, the Web graph, networks in biology, and …
A lightweight infrastructure for graph analytics
D Nguyen, A Lenharth, K Pingali - Proceedings of the twenty-fourth ACM …, 2013 - dl.acm.org
Several domain-specific languages (DSLs) for parallel graph analytics have been proposed
recently. In this paper, we argue that existing DSLs can be implemented on top of a general …
recently. In this paper, we argue that existing DSLs can be implemented on top of a general …
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 …
Graphmat: High performance graph analytics made productive
Given the growing importance of large-scale graph analytics, there is a need to improve the
performance of graph analysis frameworks without compromising on productivity. GraphMat …
performance of graph analysis frameworks without compromising on productivity. GraphMat …
Graphq: Scalable pim-based graph processing
Processing-In-Memory (PIM) architectures based on recent technology advances (eg,
Hybrid Memory Cube) demonstrate great potential for graph processing. However, existing …
Hybrid Memory Cube) demonstrate great potential for graph processing. However, existing …
Toward real-time ray tracing: A survey on hardware acceleration and microarchitecture techniques
Ray tracing has long been considered as the next-generation technology for graphics
rendering. Recently, there has been strong momentum to adopt ray tracing--based …
rendering. Recently, there has been strong momentum to adopt ray tracing--based …
Energy efficient architecture for graph analytics accelerators
Specialized hardware accelerators can significantly improve the performance and power
efficiency of compute systems. In this paper, we focus on hardware accelerators for graph …
efficiency of compute systems. In this paper, we focus on hardware accelerators for graph …