• Journal of Information Processing Systems
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• v.8 no.2
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• pp.331-346
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• 2012

Task-based programming is becoming the state-of-the-art method of choice for extracting the desired performance from multi-core chips. It expresses a program in terms of lightweight logical tasks rather than heavyweight threads. Intel Threading Building Blocks (TBB) is a task-based parallel programming paradigm for multi-core processors. The performance gain of this paradigm depends to a great extent on the efficiency of its parallel constructs. The parallel overheads incurred by parallel constructs determine the ability for creating large-scale parallel programs, especially in the case of fine-grain parallelism. This paper presents a study of TBB parallelization overheads. For this purpose, a TBB micro-benchmarks suite called TBBench has been developed. We use TBBench to evaluate the parallelization overheads of TBB on different multi-core machines and different compilers. We report in detail in this paper on the relative overheads and analyze the running results.

• Journal of Software Engineering Society
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• v.11 no.1 s.37
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• pp.3-21
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• 1998

• Journal of the Korea Society of Computer and Information
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• v.26 no.2
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• pp.1-9
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• 2021

In this paper, we extend a generic library framework based on the state monad to exploit deterministic parallelism in a purely functional language Haskell and provide benchmarks for the extended features on a multicore machine. Although purely functional programs are known to be well-suited to exploit parallelism, unintended squential data dependencies could prohibit effective parallelism. Symbolic execution programs usually implement fresh name generation in order to prevent confusion between variables in different scope with the same name. Such implementations are often based on squential state management, working against parallelism. We provide reusable primitives to help developing parallel symbolic execution programs with unbound-genercis, a generic name-binding library for Haskell, avoiding sequential dependencies in fresh name generation. Our parallel extension does not modify the internal implementation of the unbound-generics library, having zero possibility of degrading existing serial implementations of symbolic execution based on unbound-genecrics. Therefore, our extension can be applied only to the parts of source code that need parallel speedup.

• Journal of KIISE
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• v.44 no.8
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• pp.791-802
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• 2017

Competition in the modern CPU market has shifted from speeding up the clock speed of a single core to increasing the number of cores. As such, there is a growing interest in parallel programming to maximize the use of resources of many core processors. In this paper, we propose parallel programming models in Haskell to find an advisable parallel programming model for many-core environments. Specifically, we used Eval monad and Cloud Haskell to develop two versions of parallel programs: plagiarism detection and K-means. Then, we evaluated the performance of the developed programs in 32-core and 120-core environments. The results of our experiment show that the Eval monad is highly efficient in an environment with a small number of cores. On the other hand, the Cloud Haskell runtime shows 37% improvement over Eval monad and the scalability shows a 134% improvement over Eval monad as the number of cores increases.

• Geophysics and Geophysical Exploration
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• v.19 no.3
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• pp.131-135
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• 2016

Coarray is a parallel processing technique introduced in the Fortran 2008 standard. Coarray can implement parallel processing using simple syntax. In this research, we examined applicability of Coarray to seismic parallel processing by comparing performance of seismic data processing programs using Coarray and MPI. We compared calculation time using seismic wave propagation modeling and one to one communication time using domain decomposition technique. We also compared performance of parallel reverse-time migration programs using Coarray and MPI. Test results show that the computing speed of Coarray method is similar to that of MPI. On the other hand, MPI has superior communication speed to that of Coarray.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.7
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• pp.379-386
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• 2004

We designed a portable parallel library HPCL(High Performance Computing Library) with following objectives: (1) to provide a close relationship between the parallel code and the original sequential code that will help future versions of the sequential code and (2) to enhance performance of the parallel code. The library is an interface written in C and Fortran programming languages between MPI(Message Passing Interface) and parallel programs in Fortran. Performance results were determined on clusters of PC's and IBM SP4.

• The Transactions of the Korea Information Processing Society
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• v.6 no.3
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• pp.792-803
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• 1999

Most data parallel language compilers are source-to-source translators. Most Compilers of HPF which is recognized as a standard data parallel language convert a parallel program in PHF in a Fortran 77 program inserted message passing primitives. By the way, they currently generate significant amount of ineffective codes in the course of the conversion. Especially, FORALL construct is converted into several DO loops, so loop overhead of these codes is very increased. In this paper, we define and use relation distance vector to keep necessary informations. Then we evaluate and analyze execution time for the codes converted by our method and by PARADIGM method for various array sizes.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1997.11a
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• pp.79-89
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• 1997

Logic programming has many advantages as a paradigm for parallel programming because it offers ease of programming while retaining high expressive power due to its declarative semantics. In parallel logic programming, one of the important issues is the compile-time parallelism detection. Static data-dependency analysis has been widely used to gather some information needed for the detection of AND-parallelism. However, the static data-dependency analysis cannot fully detect AND-parallelism because it does not provide some necessary functions such as the propagation of groundness. As an alternative approach, abstract interpretation provides a promising way to deal with AND-parallelism detection, while a full-blown abstract interpretation is not efficient in terms of computation since it inherently employs some complex operations not necessary for gathering the information on AND-parallelism. In this paper, we propose an abstract domain which can provide a precise and efficient way to use the abstract interpretation for the detection of AND-parallelism of logic programs.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.9
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• pp.377-384
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• 2017

The performance improvement of a single core processor has reached its limit since the circuit density cannot be increased any longer due to overheating. Therefore, the multicore and manycore architectures have emerged as viable approaches and parallel programming becomes more important. Haskell, a purely functional language, is getting popular in this situation since it naturally supports parallel programming owing to its beneficial features including the implicit parallelism in evaluating expressions and the monadic tools supporting parallel constructs. However, the performance of Haskell parallel programs is strongly influenced by the performance of the run-time system including the garbage collector. Though a memory profiling tool namely GC-tune has been suggested, we need a more systematic way to use this tool. Since GC-tune finds the optimal memory size by executing the target program with all the different possible GC options, the GC-tuning time takes too long. This paper suggests a basic divide-and-conquer method to reduce the number of GC-tune executions by reducing the search area by one-quarter for every searching step. Applying this method to two parallel programs, a maximally independent set and a K-means programs, the memory tuning time is reduced by 7.78 times with accuracy 98% on average.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.1B
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• pp.27-32
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• 2003

This paper presents the Genetic Algorithm based Task Scheduling (GATS) method for the scheduling of programs with diverse embedded parallelism types in Distributed Parallel Systems, which consist of a set of loosely coupled parallel and vector machines connected via high speed networks The distributed parallel processing tries to solve computationally intensive problems that have several types of parallelism, on a suite of high performance and parallel machines in a manner that best utilizes the capabilities of each machine. When scheduling in distributed parallel systems, the matching of the parallelism characteristics between tasks and parallel machines rather than load balancing should be carefully handled with the minimization of communication cost in order to obtain more speedup. This paper proposes the based initialization methods for an initial population and the knowledge-based mutation methods to accommodate the parallelism type matching in genetic algorithms.