QSM: A general purpose shared-memory model for parallel computation

1997 ◽  
pp. 1-5 ◽  
Author(s):  
Vijaya Ramachandran
Keyword(s):  
Parallel Computation ◽  
Shared Memory ◽  
General Purpose ◽  
Memory Model

Optimally universal parallel computers

1988 ◽  
Vol 326 (1591) ◽  
pp. 373-376 ◽  
Keyword(s):  
Parallel Computation ◽  
Shared Memory ◽  
Parallel Computers ◽  
Constant Factor ◽  
Memory Model ◽  
Original Program

It is shown that any program written for the idealized shared-memory model of parallel computation can be simulated on a hypercube architecture with only constant factor inefficiency, provided that the original program has a certain amount of parallel slackness.

Can a Shared-Memory Model Serve as a Bridging Model for Parallel Computation?

1999 ◽  
Vol 32 (3) ◽  
pp. 327-359 ◽  
Author(s):  
P. B. Gibbons ◽  
Y. Matias
Keyword(s):  
Parallel Computation ◽  
Shared Memory ◽  
Memory Model ◽  
Bridging Model

scholarly journals Experiences of the GPU Thread Configuration and Shared Memory

2018 ◽  
Vol 3 (7) ◽  
pp. 12
Author(s):  
DaeHwan Kim
Keyword(s):  
Parallel Computation ◽  
Shared Memory ◽  
Memory Performance ◽  
General Purpose ◽  
Global Memory ◽  
Experimental Result ◽  
Gpu Programming ◽  
Memory Accesses

Nowadays, GPU processors are widely used for general-purpose parallel computation applications. In the GPU programming, thread and block configuration is one of the most important decisions to be made, which increases parallelism and hides instruction latency. However, in many cases, it is often difficult to have sufficient parallelism to hide all the latencies, where the high latencies are often caused by the global memory accesses. In order to reduce the number of  those accesses, the shared memory is instead used which is  much faster than the global memory being located on a chip. The performance of the proposed thread configuration is evaluated on the GPU 960 processor. The experimental result shows that the best configuration improves the performance by 7.3 times compared to the worst configuration in the experiment. The experiences are also discussed for the shared memory performance when compared to that of the global memory.

scholarly journals PPT-SASMM: Scalable Analytical Shared Memory Model

2020 ◽  
Author(s):  
Atanu Barai ◽  
Gopinath Chennupati ◽  
Nandakishore Santhi ◽  
Abdel-Hameed Badawy ◽  
Yehia Arafa ◽  
...  
Keyword(s):  
Shared Memory ◽  
Memory Model

scholarly journals A GPGPU PROGRAMMING FRAMEWORK BASED ON A SHARED-MEMORY MODEL

2013 ◽  
Vol 3 (3) ◽  
Author(s):  
Kazuhiko Ohno ◽  
Dai Michiura ◽  
Masaki Matsumoto ◽  
Takahiro Sasaki ◽  
Toshio Kondo
Keyword(s):  
Shared Memory ◽  
Memory Model ◽  
Programming Framework

A GPGPU Programming Framework based on a Shared-Memory Model

2011 ◽  
Author(s):  
Kazuhiko Ohno ◽  
Dai Michiura ◽  
Masaki Matsumoto ◽  
Takahiro Sasaki ◽  
Toshio Kondo
Keyword(s):  
Shared Memory ◽  
Memory Model ◽  
Programming Framework

scholarly journals PARALLELIZATION OF ASSEMBLY OPERATION IN FINITE ELEMENT METHOD

2020 ◽  
Vol 60 (1) ◽  
pp. 25-37
Author(s):  
Michal Bošanský ◽  
Bořek Patzák
Keyword(s):  
Finite Element ◽  
Shared Memory ◽  
Object Oriented ◽  
Memory Model ◽  
Finite Element Code ◽  
Large Problem ◽  
Assembly Operation ◽  
Finite Element Software ◽  
Multiple Threads ◽  
Assembly Operations

The efficient codes can take an advantage of multiple threads and/or processing nodes to partition a work that can be processed concurrently. This can reduce the overall run-time or make the solution of a large problem feasible. This paper deals with evaluation of different parallelization strategies of assembly operations for global vectors and matrices, which are one of the critical operations in any finite element software. Different assembly strategies for systems with a shared memory model are proposed and evaluated, using Open Multi-Processing (OpenMP), Portable Operating System Interface (POSIX), and C++11 Threads. The considered strategies are based on simple synchronization directives, various block locking algorithms and, finally, on smart locking free processing based on a colouring algorithm. The different strategies were implemented in a free finite element code with object-oriented architecture OOFEM [1].

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