Multicore C++ Standard Template Library with C++0x

Szűgyi, Zalán; Török, Márk; Pataki, Norbert; Kozsik, Tamás

doi:10.1063/1.3636868

Cited by 6 publications

(5 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The STL library has been one of the most parallelized and studied libraries in the literature. For example, the work in [18] presents an implementation for multicore architectures of the STL library using Cilk++. Works such as [19,20] developed parallel versions of this library for shared-and distributed-memory systems using OpenMP and MPI respectively.…”

Section: Related Workmentioning

confidence: 99%

“…• STL Benchmarks: The STL Library is a well-known supporting tool for developers [25] that includes many useful algorithms. During the last few years, many works have presented parallel versions of this library [26,19,20], as well as new parallel programming models that support the development or use of this library in parallel [18]. We implement the routines summarized in Tab.…”

Section: B Applying the Operators: Case Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Operators for Data Redistribution: Applications to the STL Library and RayTracing Algorithm

et al. 2021

View full text Add to dashboard Cite

In distributed-memory systems, data redistributions are operations that change the ownership and location of a selected subset of a data structure at runtime. They allow the improvement of the performance of parallel algorithms which operate on changing or partial domains, aiming to create a balanced workload among the active processes. To manually redistribute data is a cumbersome and error-prone task. In this paper, we present a method based on four combinable operators to redistribute partial domains selected by the programmer at runtime in an efficient and simple way. They abstract to the programmer the data-redistribution implementation details, such as the new mapping, relocation, and communication of the selected data. We also present the application of the proposed operators to a RayTracing application and to a significant part of STL (C++ Standard Template Library). Our experimental results show that our approach automatically generates a good load balance, which leads to performance improvements for generic data-distribution policies. It does not introduce significant performance overheads compared with tailored data redistributions directly programmed using MPI (Message Passing Interface), while it greatly reduces the code development effort.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: B Applying the Operators: Case Studiesmentioning

confidence: 99%

Operators for Data Redistribution: Applications to the STL Library and RayTracing Algorithm

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Although the language does not contain an efficient multicore library, just the C++ STL only which is an efficiency bottleneck in multicore environment. We develop a new STL implementation for Cilk++ to cope with the challenges of multicore architectures [25]. This new implementation can be safer solution, too.…”

Section: Introductionmentioning

confidence: 99%

C++ Standard Template Library by template specialized containers

Pataki¹

2011

Preprint

View full text Add to dashboard Cite

The C++ Standard Template Library is the flagship example for libraries based on the generic programming paradigm. The usage of this library is intended to minimize the number of classical C/C++ errors, but does not warrant bug-free programs. Furthermore, many new kinds of errors may arise from the inaccurate use of the generic programming paradigm, like dereferencing invalid iterators or misunderstanding remove-like algorithms.In this paper we present some typical scenarios that may cause runtime or portability problems. We emit warnings and errors while these risky constructs are used. We also present a general approach to emit "customized" warnings. We support the so-called "believe-me marks" to disable warnings. We present another typical usage of our technique, when classes become deprecated during the software lifecycle.

show abstract

“…The STL library has been one of the most parallelized and studied libraries in the literature. For example, the work in [129] presents an implementation for multicore architectures of the STL library using Cilk++. Works like [120,123] developed parallel versions of this library for shared-and distributed-memory systems using OpenMP and MPI respectively.…”

Section: Parallel Librariesmentioning

confidence: 99%

“…• STL Benchmarks: The STL Library is a well-known supporting tool for developers [122], that includes many useful algorithms. During the last years, many works have presented parallel versions of this library [50,120,123], as well as new parallel programming models that support the development or the use of this library in parallel [129]. We have implemented using the operators, the routines summarized in Tab.…”

Section: Applying the Operators: Case Studiesmentioning

confidence: 99%

Easing parallel programming on heterogeneous systems

Fernández¹

View full text Add to dashboard Cite

The use of parallel computing systems frequently represents the only scalable way to solve HPC (High performance Computing) problems in reasonable execution times. The current trend in high performance computing platforms is to include in the same machine several parallel devices, of different type and architectures, and to interconnect them to form highly parallel and heterogeneous distributed systems. Programming efficient and portable parallel applications that can really exploit these systems, imposes specific and complex challenges to the programmers. A programmer must be proficient in distributed-memory communication tools or layers, shared-memory programming models, and specific programming models for the available co-processors, in order to create hybrid programs that will exploit all the machine capabilities. Moreover, she also has to deal with the proper workload distribution among the different nodes and devices, assigning to each one an amount of workload related to their computation power and features. Nowadays, all these issues should be solved by the programmer, making the programming of heterogeneous platforms an actual challenge. This PhD. Thesis addresses several main problems related to the parallel programming for highly heterogeneous and distributed systems. It first tackles problems to allow the developing of efficient coordination codes, portable across different kind of devices, accelerators, and architectures. Then, it also targets problems related to the data communication and partition issues concerning the use of devices in distributed-memory systems. In this dissertation we introduce abstractions, mechanisms, and methods to solve many of these problems. We also discuss their practical application to develop research prototypes and actual programming tools. Experimental works conducted using these tools validates the applicability of the proposed techniques and the portability, efficiency, and versatility of the programs that can be obtained.

show abstract

Multicore C++ Standard Template Library with C++0x

Cited by 6 publications

References 2 publications

Operators for Data Redistribution: Applications to the STL Library and RayTracing Algorithm

Operators for Data Redistribution: Applications to the STL Library and RayTracing Algorithm

C++ Standard Template Library by template specialized containers

Easing parallel programming on heterogeneous systems

Contact Info

Product

Resources

About