On privatization of variables for data-parallel execution

2002 ◽  
Author(s):  
M. Gupta
Keyword(s):  
Parallel Execution ◽  
Data Parallel

scholarly journals Comparative Evaluation and Case Studies of Shared-Memory and Data-Parallel Execution Patterns

1999 ◽  
Vol 7 (1) ◽  
pp. 1-19
Author(s):  
Xiaodong Zhang ◽  
Lin Sun
Keyword(s):  
Linear System ◽  
Shared Memory ◽  
Interconnection Networks ◽  
Parallel Execution ◽  
Parallel Model ◽  
Scientific Applications ◽  
Data Parallel ◽  
High Level ◽  
Access Patterns ◽  
Structured Program

Shared‐memory and data‐parallel programming models are two important paradigms for scientific applications. Both models provide high‐level program abstractions, and simple and uniform views of network structures. The common features of the two models significantly simplify program coding and debugging for scientific applications. However, the underlining execution and overhead patterns are significantly different between the two models due to their programming constraints, and due to different and complex structures of interconnection networks and systems which support the two models. We performed this experimental study to present implications and comparisons of execution patterns on two commercial architectures. We implemented a standard electromagnetic simulation program (EM) and a linear system solver using the shared‐memory model on the KSR‐1 and the data‐parallel model on the CM‐5. Our objectives are to examine the execution pattern changes required for an implementation transformation between the two models; to study memory access patterns; to address scalability issues; and to investigate relative costs and advantages/disadvantages of using the two models for scientific computations. Our results indicate that the EM program tends to become computation‐intensive in the KSR‐1 shared‐memory system, and memory‐demanding in the CM‐5 data‐parallel system when the systems and the problems are scaled. The EM program, a highly data‐parallel program performed extremely well, and the linear system solver, a highly control‐structured program suffered significantly in the data‐parallel model on the CM‐5. Our study provides further evidence that matching execution patterns of algorithms to parallel architectures would achieve better performance.

A HYBRID SHARED MEMORY EXECUTION MODEL FOR A DATA PARALLEL LANGUAGE WITH I/O

2008 ◽  
Vol 18 (01) ◽  
pp. 23-37 ◽  
Author(s):  
CLEMENS GRELCK ◽  
STEFFEN KUTHE ◽  
SVEN-BODO SCHOLZ
Keyword(s):  
Program Analysis ◽  
Flow Characteristics ◽  
Parallel Execution ◽  
Parallel Language ◽  
Data Parallel ◽  
Execution Model ◽  
Execution Mode ◽  
Parallel Code ◽  
Execution Models ◽  
Compilation Techniques

We propose a novel execution model for the implicitly parallel execution of data parallel programs in the presence of general I/O operations. This model is called hybrid because it combines the advantages of the standard execution models fork/join and SPMD. Based on program analysis the hybrid model adapts itself to one or the other on the granularity of individual instructions. We outline compilation techniques that systematically derive the organization of parallel code from data flow characteristics aiming at the reduction of execution mode switches in general and synchronization/communication requirements in particular. Experiments based on a prototype implementation show the effectiveness of the hybrid execution model for reducing parallel overhead.

Parallel functional programming on recursively defined data via data-parallel recursion

1999 ◽  
Vol 9 (4) ◽  
pp. 427-462 ◽  
Author(s):  
SUSUMU NISHIMURA ◽  
ATSUSHI OHORI
Keyword(s):  
Parallel Processing ◽  
Functional Programming ◽  
Formal Semantics ◽  
Operational Semantics ◽  
Type System ◽  
Parallel Execution ◽  
System Of Equations ◽  
Functional Language ◽  
Data Parallel ◽  
Evaluation Mechanism

This article proposes a new language mechanism for data-parallel processing of dynamically allocated recursively defined data. Different from the conventional array-based data- parallelism, it allows parallel processing of general recursively defined data such as lists or trees in a functional way. This is achieved by representing a recursively defined datum as a system of equations, and defining new language constructs for parallel transformation of a system of equations. By integrating them with a higher-order functional language, we obtain a functional programming language suitable for describing data-parallel algorithms on recursively defined data in a declarative way. The language has an ML style polymorphic type system and a type sound operational semantics that uniformly integrates the parallel evaluation mechanism with the semantics of a typed functional language. We also show the intended parallel execution model behind the formal semantics, assuming an idealized distributed memory multicomputer.

DISTRIBUTED EVALUATION OF FUNCTIONAL BSP PROGRAMS

2001 ◽  
Vol 11 (04) ◽  
pp. 423-437 ◽  
Author(s):  
F. LOULERGUE
Keyword(s):  
Programming Model ◽  
Parallel Execution ◽  
Parallel Composition ◽  
Bulk Synchronous Parallel ◽  
Data Parallel ◽  
Parallel Languages ◽  
Execution Model ◽  
Points Of View ◽  
Distributed Evaluation ◽  
Value Strategy

The BS λp-calculus is a calculus of functional bulk synchronous parallel (BSP) programs. It is the basis for the design of a bulk synchronous parallel ML language. For data-parallel languages, there are two points of view: the programming model where a program is seen as a sequence of operations on parallel vectors, and the execution model where the program is a parallel composition of programs run on each processor of the parallel machine. BSP algorithms are defined by data-parallel algorithms with explicit (physical) processes in order to allow their parallel execution time to be estimated. We present here a distributed evaluation minimally synchronous for BSP execution (which corresponds to the execution model). This distributed evaluation is correct w.r.t. the call-by-value strategy of the BS λp-calculus (which corresponds to the programming model).

SIMD stealing: Architectural support for efficient data parallel execution on multicores

2019 ◽  
Vol 65 ◽  
pp. 136-147
Author(s):  
Libo Huang ◽  
Yashuai Lü ◽  
Sheng Ma ◽  
Nong Xiao ◽  
Zhiying Wang
Keyword(s):  
Parallel Execution ◽  
Architectural Support ◽  
Data Parallel ◽  
Efficient Data
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