Techniques for Efficient Execution of Fine-grained Massively-Parallel Programs.

1997 ◽  
Author(s):  
Andrew A. Chien
Keyword(s):  
Parallel Programs ◽  
Massively Parallel ◽  
Fine Grained ◽  
Efficient Execution

Visualizing the behavior of massively parallel programs

1991 ◽  
Author(s):  
Mark Friedell ◽  
Mark LaPolla ◽  
Sandeep Kochhar ◽  
Steve Sistare ◽  
Janusz Juda
Keyword(s):  
Parallel Programs ◽  
Massively Parallel

Linking Compilation and Visualization for Massively Parallel Programs

2007 ◽  
Author(s):  
Alex K. Jones ◽  
Raymond R. Hoare ◽  
Joseph St. Onge ◽  
Joshua Lucas ◽  
Shuyi Shao ◽  
...  
Keyword(s):  
Parallel Programs ◽  
Massively Parallel

Degree-of-Node Task Scheduling of Fine-Grained Parallel Programs on Heterogeneous Systems

2019 ◽  
Vol 34 (5) ◽  
pp. 1096-1108
Author(s):  
Han Lin ◽  
Ming-Fan Li ◽  
Cheng-Fan Jia ◽  
Jun-Nan Liu ◽  
Hong An
Keyword(s):  
Task Scheduling ◽  
Heterogeneous Systems ◽  
Parallel Programs ◽  
Fine Grained

On the proper treatment of connectionism

1988 ◽  
Vol 11 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Paul Smolensky
Keyword(s):  
Cognitive Modeling ◽  
Cognitive Models ◽  
Massively Parallel ◽  
Neural Models ◽  
Physical Sciences ◽  
Connectionist Models ◽  
Fine Grained ◽  
Sequential Rule ◽  
Computational Systems ◽  
Level Of Analysis

AbstractA set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models.Higher-level analyses of these connectionist models reveal subtle relations to symbolic models. Parallel connectionist memory and linguistic processes are hypothesized to give rise to processes that are describable at a higher level as sequential rule application. At the lower level, computation has the character of massively parallel satisfaction of soft numerical constraints; at the higher level, this can lead to competence characterizable by hard rules. Performance will typically deviate from this competence since behavior is achieved not by interpreting hard rules but by satisfying soft constraints. The result is a picture in which traditional and connectionist theoretical constructs collaborate intimately to provide an understanding of cognition.

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