Investigations for a Josephson Computer Main Memory with Single-Flux-Quantum Cells

1980 ◽  
Vol 24 (2) ◽  
pp. 155-166 ◽  
Author(s):  
P. Gueret ◽  
A. Moser ◽  
P. Wolf
Keyword(s):  
Main Memory ◽  
Flux Quantum ◽  
Quantum Cells

Design of single flux quantum cells for a 10-Nb-layer process

2009 ◽  
Vol 469 (15-20) ◽  
pp. 1670-1673 ◽  
Author(s):  
H. Akaike ◽  
M. Tanaka ◽  
K. Takagi ◽  
I. Kataeva ◽  
R. Kasagi ◽  
...  
Keyword(s):  
Flux Quantum ◽  
Quantum Cells

Rapid Single-Flux-Quantum NOR Logic Gate Realized through the Use of Toggle Storage Loop

2020 ◽  
Vol E103.C (10) ◽  
pp. 547-549
Author(s):  
Yoshinao MIZUGAKI ◽  
Koki YAMAZAKI ◽  
Hiroshi SHIMADA
Keyword(s):  
Logic Gate ◽  
Flux Quantum ◽  
Rapid Single Flux Quantum ◽  
Nor Logic Gate

Proposal of a Compact Neutron Diffraction System with a Single-Flux-Quantum Signal Processor

2011 ◽  
Vol E94-C (3) ◽  
pp. 254-259 ◽  
Author(s):  
Akira FUJIMAKI ◽  
Isao NAKANISHI ◽  
Shigeyuki MIYAJIMA ◽  
Kohei ARAI ◽  
Yukio AKITA ◽  
...  
Keyword(s):  
Neutron Diffraction ◽  
Flux Quantum ◽  
Signal Processor

AgiMonitor: a remote database monitoring tool for main memory database

2014 ◽  
Author(s):  
Huazhuang Yao ◽  
Yongyan Wang ◽  
Shuai Wang ◽  
Kun Li ◽  
Chao Guo
Keyword(s):  
Main Memory ◽  
Monitoring Tool ◽  
Main Memory Database

Advanced Single Flux Quantum Devices

1999 ◽  
Author(s):  
Konstantin K. Likharev ◽  
P. Bunyk ◽  
W. Chao ◽  
T. Filippov ◽  
Y. Kameda
Keyword(s):  
Flux Quantum ◽  
Quantum Devices

scholarly journals Efficient local locking for massively multithreaded in-memory hash-based operators

2021 ◽  
Author(s):  
Bashar Romanous ◽  
Skyler Windh ◽  
Ildar Absalyamov ◽  
Prerna Budhkar ◽  
Robert Halstead ◽  
...  
Keyword(s):  
Relational Databases ◽  
Aggregation Operators ◽  
Main Memory ◽  
Paradigm Shifts ◽  
Multithreaded Processors ◽  
Cache Hierarchies ◽  
Processor Architectures ◽  
Spatial Locality ◽  
Content Addressable Memories ◽  
Multi Core Processor

AbstractThe join and group-by aggregation are two memory intensive operators that are affecting the performance of relational databases. Hashing is a common approach used to implement both operators. Recent paradigm shifts in multi-core processor architectures have reinvigorated research into how the join and group-by aggregation operators can leverage these advances. However, the poor spatial locality of the hashing approach has hindered performance on multi-core processor architectures which rely on using large cache hierarchies for latency mitigation. Multithreaded architectures can better cope with poor spatial locality by masking memory latency with many outstanding requests. Nevertheless, the number of parallel threads, even in the most advanced multithreaded processors, such as UltraSPARC, is not enough to fully cover the main memory access latency. In this paper, we explore the hardware re-configurability of FPGAs to enable deeper execution pipelines that maintain hundreds (instead of tens) of outstanding memory requests across four FPGAs-drastically increasing concurrency and throughput. We present two end-to-end in-memory accelerators for the join and group-by aggregation operators using FPGAs. Both accelerators use massive multithreading to mask long memory delays of traversing linked-list data structures, while concurrently managing hundreds of thread states across four FPGAs locally. We explore how content addressable memories can be intermixed within our multithreaded designs to act as a synchronizing cache, which enforces locks and merges jobs together before they are written to memory. Throughput results for our hash-join operator accelerator show a speedup between 2$$\times $$ × and 3.4$$\times $$ × over the best multi-core approaches with comparable memory bandwidths on uniform and skewed datasets. The accelerator for the hash-based group-by aggregation operator demonstrates that leveraging CAMs achieves average speedup of 3.3$$\times $$ × with a best case of 9.4$$\times $$ × in terms of throughput over CPU implementations across five types of data distributions.

scholarly journals A Hybrid Approach Combining R*-Tree and k-d Trees to Improve Linked Open Data Query Performance

2021 ◽  
Vol 11 (5) ◽  
pp. 2405
Author(s):  
Yuxiang Sun ◽  
Tianyi Zhao ◽  
Seulgi Yoon ◽  
Yongju Lee
Keyword(s):  
Flash Memory ◽  
Query Language ◽  
Hybrid Approach ◽  
Open Data ◽  
Main Memory ◽  
Linked Open Data ◽  
Index Structure ◽  
Identification Algorithm ◽  
Distributed Computing Systems ◽  
Query Performance

Semantic Web has recently gained traction with the use of Linked Open Data (LOD) on the Web. Although numerous state-of-the-art methodologies, standards, and technologies are applicable to the LOD cloud, many issues persist. Because the LOD cloud is based on graph-based resource description framework (RDF) triples and the SPARQL query language, we cannot directly adopt traditional techniques employed for database management systems or distributed computing systems. This paper addresses how the LOD cloud can be efficiently organized, retrieved, and evaluated. We propose a novel hybrid approach that combines the index and live exploration approaches for improved LOD join query performance. Using a two-step index structure combining a disk-based 3D R*-tree with the extended multidimensional histogram and flash memory-based k-d trees, we can efficiently discover interlinked data distributed across multiple resources. Because this method rapidly prunes numerous false hits, the performance of join query processing is remarkably improved. We also propose a hot-cold segment identification algorithm to identify regions of high interest. The proposed method is compared with existing popular methods on real RDF datasets. Results indicate that our method outperforms the existing methods because it can quickly obtain target results by reducing unnecessary data scanning and reduce the amount of main memory required to load filtering results.

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