DPISCAN: Distributed and parallel architecture with indexing for structural clustering of massive dynamic graphs

Recent events in the high-performance computing industry have concerned scientists and the general public regarding a crisis or a lack of leadership in the field. That concern is understandable considering the industry's history from 1993 to 1996. Cray Research, the historic leader in supercomputing technology, was unable to survive financially as an independent company and was acquired by Silicon Graphics. Two ambitious new companies that introduced new technologies in the late 1980s and early 1990s—Thinking Machines and Kendall Square Research—were commercial failures and went out of business. And Intel, which introduced its Paragon supercomputer in 1994, discontinued production only two years later.During the same time frame, scientists who had finished the laborious task of writing scientific codes to run on vector parallel supercomputers learned that those codes would have to be rewritten if they were to run on the next-generation, highly parallel architecture. Scientists who are not yet involved in high-performance computing are understandably hesitant about committing their time and energy to such an apparently unstable enterprise.However, beneath the commercial chaos of the last several years, a technological revolution has been occurring. The good news is that the revolution is over, leading to five to ten years of predictable stability, steady improvements in system performance, and increased productivity for scientific applications. It is time for scientists who were sitting on the fence to jump in and reap the benefits of the new technology.

Download Full-text

PROTEUS: A High-Performance Parallel-Architecture Simulator

10.21236/ada241343 ◽

1991 ◽

Cited By ~ 99

Author(s):

Eric A. Brewer ◽

Chrysanthos N. Dellarocas ◽

Adrian Colbrook ◽

William E. Weihl

Keyword(s):

High Performance ◽

Parallel Architecture

Download Full-text

Flexible 5G New Radio LDPC Encoder Optimized for High Hardware Usage Efficiency

Electronics ◽

10.3390/electronics10091106 ◽

2021 ◽

Vol 10 (9) ◽

pp. 1106

Author(s):

Vladimir L. Petrović ◽

Dragomir M. El Mezeni ◽

Andreja Radošević

Keyword(s):

Channel Coding ◽

Parallel Architecture ◽

Ldpc Codes ◽

Parity Check ◽

Case Scenario ◽

New Radio ◽

Physical Channel ◽

Computationally Intensive ◽

Hardware Processing ◽

Usage Efficiency

Quasi-cyclic low-density parity-check (QC–LDPC) codes are introduced as a physical channel coding solution for data channels in 5G new radio (5G NR). Depending on the use case scenario, this standard proposes the usage of a wide variety of codes, which imposes the need for high encoder flexibility. LDPC codes from 5G NR have a convenient structure and can be efficiently encoded using forward substitution and without computationally intensive multiplications with dense matrices. However, the state-of-the-art solutions for encoder hardware implementation can be inefficient since many hardware processing units stay idle during the encoding process. This paper proposes a novel partially parallel architecture that can provide high hardware usage efficiency (HUE) while achieving encoder flexibility and support for all 5G NR codes. The proposed architecture includes a flexible circular shifting network, which is capable of shifting a single large bit vector or multiple smaller bit vectors depending on the code. The encoder architecture was built around the shifter in a way that multiple parity check matrix elements can be processed in parallel for short codes, thus providing almost the same level of parallelism as for long codes. The processing schedule was optimized for minimal encoding time using the genetic algorithm. The optimized encoder provided high throughputs, low latency, and up-to-date the best HUE.

Download Full-text

Parallel architecture and compilation techniques

ACM SIGARCH Computer Architecture News ◽

10.1145/563647.563651 ◽

2001 ◽

Vol 29 (5) ◽

pp. 9-12

Author(s):

S. Bartolini ◽

R. Giorgi ◽

J. Protic ◽

C. A. Prete ◽

M. Valero

Keyword(s):

Parallel Architecture ◽

Compilation Techniques

Download Full-text

Bloom Filter-Based Parallel Architecture for Accelerating Equi-Join Operation on FPGA

Electronics ◽

10.3390/electronics10151778 ◽

2021 ◽

Vol 10 (15) ◽

pp. 1778

Author(s):

Binhao He ◽

Meiting Xue ◽

Shubiao Liu ◽

Wei Luo

Keyword(s):

Relational Databases ◽

Parallel Architecture ◽

Operation Time ◽

Bloom Filter ◽

Search Tree ◽

Binary Search Tree ◽

Maximum Acceleration ◽

Data Intensive ◽

Match Rate ◽

Field Programmable

As one of the most important operations in relational databases, the join is data-intensive and time-consuming. Thus, offloading this operation using field-programmable gate arrays (FPGAs) has attracted much interest and has been broadly researched in recent years. However, the available SRAM-based join architectures are often resource-intensive, power-consuming, or low-throughput. Besides, a lower match rate does not lead to a shorter operation time. To address these issues, a Bloom filter (BF)-based parallel join architecture is presented in this paper. This architecture first leverages the BF to discard the tuples that are not in the join result and classifies the remaining tuples into different channels. Second, a binary search tree is used to reduce the number of comparisons. The proposed method was implemented on a Xilinx FPGA, and the experimental results show that under a match rate of 50%, our architecture achieved a high join throughput of 145.8 million tuples per second and a maximum acceleration factor of 2.3 compared to the existing SRAM-based join architectures.

Download Full-text

Hierarchical Graph Convolutional Network for Data Evaluation of Dynamic Graphs

2020 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata50022.2020.9377789 ◽

2020 ◽

Author(s):

Bin Wang ◽

Teruaki Hayashi ◽

Yukio Ohsawa

Keyword(s):

Data Evaluation ◽

Dynamic Graphs ◽

Convolutional Network ◽

Hierarchical Graph

Download Full-text

A FPGA based intra-parallel architecture for PageRank graph processing

2020 IEEE International Conference on Edge Computing (EDGE) ◽

10.1109/edge50951.2020.00012 ◽

2020 ◽

Author(s):

Mei Guoqiang ◽

Hao Rui ◽

Wang Jiangwei ◽

Kan Hongwei ◽

Li Rengang

Keyword(s):

Parallel Architecture ◽

Graph Processing

Download Full-text

Approximate Mining of Frequent -Subgraph Patterns in Evolving Graphs

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3442590 ◽

2021 ◽

Vol 15 (3) ◽

pp. 1-35

Author(s):

Muhammad Anis Uddin Nasir ◽

Cigdem Aslay ◽

Gianmarco De Francisci Morales ◽

Matteo Riondato

Keyword(s):

Sample Size ◽

State Of The Art ◽

Empirical Evaluation ◽

Connected Subgraph ◽

Dynamic Graphs ◽

High Quality ◽

Complexity Bounds ◽

Approximation Quality ◽

Uniform Sample ◽

Waiting For Godot

“Perhaps he could dance first and think afterwards, if it isn’t too much to ask him.” S. Beckett, Waiting for Godot Given a labeled graph, the collection of -vertex induced connected subgraph patterns that appear in the graph more frequently than a user-specified minimum threshold provides a compact summary of the characteristics of the graph, and finds applications ranging from biology to network science. However, finding these patterns is challenging, even more so for dynamic graphs that evolve over time, due to the streaming nature of the input and the exponential time complexity of the problem. We study this task in both incremental and fully-dynamic streaming settings, where arbitrary edges can be added or removed from the graph. We present TipTap , a suite of algorithms to compute high-quality approximations of the frequent -vertex subgraphs w.r.t. a given threshold, at any time (i.e., point of the stream), with high probability. In contrast to existing state-of-the-art solutions that require iterating over the entire set of subgraphs in the vicinity of the updated edge, TipTap operates by efficiently maintaining a uniform sample of connected -vertex subgraphs, thanks to an optimized neighborhood-exploration procedure. We provide a theoretical analysis of the proposed algorithms in terms of their unbiasedness and of the sample size needed to obtain a desired approximation quality. Our analysis relies on sample-complexity bounds that use Vapnik–Chervonenkis dimension, a key concept from statistical learning theory, which allows us to derive a sufficient sample size that is independent from the size of the graph. The results of our empirical evaluation demonstrates that TipTap returns high-quality results more efficiently and accurately than existing baselines.

Download Full-text