scholarly journals A practical constructive scheme for deterministic shared-memory access

1993 ◽  
Author(s):  
A. Pietracaprina ◽  
F. P. Preparata
Keyword(s):  
Shared Memory ◽  
Memory Access

COMPLEXITY OF VERIFYING JAVA SHARED MEMORY EXECUTION

2003 ◽  
Vol 13 (04) ◽  
pp. 721-733 ◽  
Author(s):  
ALEX GONTMAKHER ◽  
SERGEY POLYAKOV ◽  
ASSAF SCHUSTER
Keyword(s):  
Shared Memory ◽  
Memory Access ◽  
Np Complete

This paper studies the problem of testing shared memory Java implementations to determine whether the memory behavior they provide is consistent. The complexity of the task is analyzed. The problem is defined as that of analyzing memory access traces. The study showed that the problem is NP-complete, both in the general case and in some particular cases in which the number of memory operations per thread, the number of write operations per variable, and the number of variables are restricted.

Shared Memory Access—Boon for Graphics

1988 ◽  
Vol 5 (11) ◽  
pp. 455-458
Author(s):  
Chitra Dorai ◽  
V N Shukla
Keyword(s):  
Shared Memory ◽  
Memory Access

Multiprocessor shared memory access and rewards

1998 ◽  
Vol 335 (4) ◽  
pp. 629-641 ◽  
Author(s):  
Joann M. Paul ◽  
Marlin H. Mickle
Keyword(s):  
Shared Memory ◽  
Memory Access

VM-based shared memory on low-latency, remote-memory-access networks

1997 ◽  
Vol 25 (2) ◽  
pp. 157-169 ◽  
Author(s):  
Leonidas Kontothanassis ◽  
Galen Hunt ◽  
Robert Stets ◽  
Nikolaos Hardavellas ◽  
Michał Cierniak ◽  
...  
Keyword(s):  
Shared Memory ◽  
Access Networks ◽  
Memory Access ◽  
Remote Memory ◽  
Low Latency ◽  
Remote Memory Access

scholarly journals Data structures access model for remote shared memory

2021 ◽  
Vol 244 ◽  
pp. 07001
Author(s):  
Anatoliy Nyrkov ◽  
Konstantin Ianiushkin ◽  
Andrey Nyrkov ◽  
Yulia Romanova ◽  
Vagiz Gaskarov
Keyword(s):  
Shared Memory ◽  
Data Structures ◽  
Data Model ◽  
High Performance ◽  
Direct Memory Access ◽  
Performance Comparison ◽  
Memory Access ◽  
Memory Storage ◽  
Race Conditions ◽  
Performance Computing

Recent achievements in high-performance computing significantly narrow the performance gap between single and multi-node computing, and open up opportunities for systems with remote shared memory. The combination of in-memory storage, remote direct memory access and remote calls requires rethinking how data organized, protected and queried in distributed systems. Reviewed models let us implement new interpretations of distributed algorithms allowing us to validate different approaches to avoid race conditions, decrease resource acquisition or synchronization time. In this paper, we describe the data model for mixed memory access with analysis of optimized data structures. We also provide the result of experiments, which contain a performance comparison of data structures, operating with different approaches, evaluate the limitations of these models, and show that the model does not always meet expectations. The purpose of this paper to assist developers in designing data structures that will help to achieve architectural benefits or improve the design of existing distributed system.

scholarly journals Memory Access Behavior Analysis of NUMA-Based Shared Memory Programs

2002 ◽  
Vol 10 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Jie Tao ◽  
Wolfgang Karl ◽  
Martin Schulz
Keyword(s):  
Shared Memory ◽  
Data Locality ◽  
Memory Access ◽  
Remote Memory ◽  
Data Layout ◽  
Performance Improvements ◽  
Significant Performance ◽  
Working Set ◽  
Memory Accesses ◽  
Memory Applications

Shared memory applications running transparently on top of NUMA architectures often face severe performance problems due to bad data locality and excessive remote memory accesses. Optimizations with respect to data locality are therefore necessary, but require a fundamental understanding of an application's memory access behavior. The information necessary for this cannot be obtained using simple code instrumentation due to the implicit nature of the communication handled by the NUMA hardware, the large amount of traffic produced at runtime, and the fine access granularity in shared memory codes. In this paper an approach to overcome these problems and thereby to enable an easy and efficient optimization process is presented. Based on a low-level hardware monitoring facility in coordination with a comprehensive visualization tool, it enables the generation of memory access histograms capable of showing all memory accesses across the complete address space of an application's working set. This information can be used to identify access hot spots, to understand the dynamic behavior of shared memory applications, and to optimize applications using an application specific data layout resulting in significant performance improvements.

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