A parallel architecture for AI nonlinear planning

2003 ◽  
Author(s):  
S. Lee ◽  
K. Chung
Keyword(s):  
Parallel Architecture ◽  
Nonlinear Planning

A PARALLEL ARCHITECTURE FOR AI NONLINEAR PLANNING

1990 ◽  
Vol 04 (02) ◽  
pp. 215-232 ◽  
Author(s):  
SUKHAN LEE ◽  
KYUSIK CHUNG
Keyword(s):  
Conflict Resolution ◽  
Distributed Processing ◽  
Parallel Architecture ◽  
State Level ◽  
Conflict Detection ◽  
Parallel And Distributed Processing ◽  
Resource Level ◽  
Planning Algorithm ◽  
Conflict Detection And Resolution ◽  
Nonlinear Planning

This paper presents a resource-level conflict detection and conflict resolution scheme which is combined with a state-level backward planning algorithm and provides efficient conflict detection and global conflict resolution for nonlinear planning. The scheme keeps track of the usage of individual resources during planning, and constructs a Resource-Usage Flow (RUF) structure (based on which conflict detection and resolution is accomplished). The RUF structure allows the system to perform minimal and nonredundant operations for conflict detection and resolution. Furthermore, resource-level conflict detection and resolution facilitates problem decomposition in terms of resources, thereby providing easy implementation in a parallel and distributed processing environment. Performance analysis indicates that the proposed architecture has a speed-up factor of the average depth of a plan network, D(Na), compared to the distributed NOAH, where Na (the total number of action nodes at the completion of planning) and D(Na) are considerably larger than the number of resources involved in planning as well as the number of initial goal states.

The Recent Revolution in High Performance Computing

MRS Bulletin ◽  
1997 ◽  
Vol 22 (10) ◽  
pp. 5-6
Author(s):  
Horst D. Simon
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
New Technologies ◽  
New Technology ◽  
Parallel Architecture ◽  
Time Frame ◽  
Good News ◽  
Computing Industry ◽  
Time And Energy ◽  
Performance Computing

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.

PROTEUS: A High-Performance Parallel-Architecture Simulator

1991 ◽  
Author(s):  
Eric A. Brewer ◽  
Chrysanthos N. Dellarocas ◽  
Adrian Colbrook ◽  
William E. Weihl
Keyword(s):  
High Performance ◽  
Parallel Architecture

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

Electronics ◽  
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.

Parallel architecture and compilation techniques

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

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

Electronics ◽  
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.

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