ScaleGraph: A high-performance library for billion-scale graph analytics

2015 ◽  
Author(s):  
Toyotaro Suzumura ◽  
Koji Ueno
Keyword(s):  
High Performance ◽  
Graph Analytics

High-Performance and Energy-Efficient Network-on-Chip Architectures for Graph Analytics

2016 ◽  
Vol 15 (4) ◽  
pp. 1-26 ◽  
Author(s):  
Karthi Duraisamy ◽  
Hao Lu ◽  
Partha Pratim Pande ◽  
Ananth Kalyanaraman
Keyword(s):  
Energy Efficient ◽  
High Performance ◽  
Network On Chip ◽  
Graph Analytics ◽  
On Chip

scholarly journals Grafs: declarative graph analytics

2021 ◽  
Vol 5 (ICFP) ◽  
pp. 1-32
Author(s):  
Farzin Houshmand ◽  
Mohsen Lesani ◽  
Keval Vora
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Kernel Functions ◽  
Runtime Systems ◽  
Graph Processing ◽  
Large Graphs ◽  
Graph Analytics ◽  
Efficient Code ◽  
High Level ◽  
Abstract Interface

Graph analytics elicits insights from large graphs to inform critical decisions for business, safety and security. Several large-scale graph processing frameworks feature efficient runtime systems; however, they often provide programming models that are low-level and subtly different from each other. Therefore, end users can find implementation and specially optimization of graph analytics error-prone and time-consuming. This paper regards the abstract interface of the graph processing frameworks as the instruction set for graph analytics, and presents Grafs, a high-level declarative specification language for graph analytics and a synthesizer that automatically generates efficient code for five high-performance graph processing frameworks. It features novel semantics-preserving fusion transformations that optimize the specifications and reduce them to three primitives: reduction over paths, mapping over vertices and reduction over vertices. Reductions over paths are commonly calculated based on push or pull models that iteratively apply kernel functions at the vertices. This paper presents conditions, parametric in terms of the kernel functions, for the correctness and termination of the iterative models, and uses these conditions as specifications to automatically synthesize the kernel functions. Experimental results show that the generated code matches or outperforms handwritten code, and that fusion accelerates execution.

scholarly journals A Survey on Domain-Specific Memory Architectures

2021 ◽  
Vol 16 (2) ◽  
pp. 1-9
Author(s):  
Stephanie Soldavini ◽  
Christian Pilato
Keyword(s):  
High Performance ◽  
Current Trend ◽  
Security And Privacy ◽  
Graph Analytics ◽  
Domain Specific ◽  
Specific Memory ◽  
Memory Modules ◽  
Proper Design ◽  
Efficient Memory ◽  
Memory Architectures

The never-ending demand for high performance and energy efficiency is pushing designers towards an increasing level of heterogeneity and specialization in modern computing systems. In such systems, creating efficient memory architectures is one of the major opportunities for optimizing modern workloads (e.g., computer vision, machine learning, graph analytics, etc.) that are extremely data-driven. However, designers demand proper design methods to tackle the increasing design complexity and address several new challenges, like the security and privacy of the data to be elaborated.This paper overviews the current trend for the design of domain-specific memory architectures. Domain-specific architectures are tailored for the given application domain, with the introduction of hardware accelerators and custom memory modules while maintaining a certain level of flexibility. We describe the major components, the common challenges, and the state-of-the-art design methodologies for building domain-specific memory architectures. We also discuss the most relevant research projects, providing a classification based on our main topics.

scholarly journals HPGraph: High-Performance Graph Analytics with Productivity on the GPU

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Haoduo Yang ◽  
Huayou Su ◽  
Qiang Lan ◽  
Mei Wen ◽  
Chunyuan Zhang
Keyword(s):  
Graph Algorithms ◽  
High Performance ◽  
Gpu Computing ◽  
Programming Model ◽  
Sparse Matrix ◽  
Graph Analytics ◽  
Matrix Operations ◽  
High Level ◽  
Broad Interest

The growing use of graph in many fields has sparked a broad interest in developing high-level graph analytics programs. Existing GPU implementations have limited performance with compromising on productivity. HPGraph, our high-performance bulk-synchronous graph analytics framework based on the GPU, provides an abstraction focused on mapping vertex programs to generalized sparse matrix operations on GPU as the backend. HPGraph strikes a balance between performance and productivity by coupling high-performance GPU computing primitives and optimization strategies with a high-level programming model for users to implement various graph algorithms with relatively little effort. We evaluate the performance of HPGraph for four graph primitives (BFS, SSSP, PageRank, and TC). Our experiments show that HPGraph matches or even exceeds the performance of high-performance GPU graph libraries such as MapGraph, nvGraph, and Gunrock. HPGraph also runs significantly faster than advanced CPU graph libraries.

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