A generic parallel pattern interface for stream and data processing

Abstract

SummaryCurrent parallel programming frameworks aid developers to a great extent in implementing applications that exploit parallel hardware resources. Nevertheless, developers require additional expertise to properly use and tune them to operate efficiently on specific parallel platforms. On the other hand, porting applications between different parallel programming models and platforms is not straightforward and demands considerable efforts and specific knowledge.Apart from that, the lack of high-level parallel pattern abstractions, in those frameworks, further increases the complexity in developing parallel applications. To pave the way in this direction, this paper proposes GRPPI, a generic and reusable parallel pattern interface for both stream processing and data-intensive C++ applications. GRPPI accommodates a layer between developers and existing parallel programming frameworks targeting multi-core processors, such as C++ threads, OpenMP and Intel TBB, and accelerators, as CUDA Thrust. Furthermore, thanks to its high-level C++ application programming interface and pattern composability features, GRPPI allows users to easily expose parallelism via standalone patterns or patterns compositions matching in sequential applications. We evaluate this interface using an image processing use case and demonstrate its benefits from the usability, flexibility, and performance points of view. Furthermore, we analyze the impact of using stream and data pattern compositions on CPUs, GPUs and heterogeneous configurations. An approach to relieve developers from this burden is the use of pattern-based parallel programming frameworks, such as SkePU, 2FastFlow 3 , or Intel TBB. 4 In this sense, design patterns provide a way to encapsulate (using a building blocks approach) algorithmic aspects, allowing users to implement more robust, readable, and portable solutions with such a high-level of abstraction. Basically, these patterns instantiate parallelism while hide away the complexity of concurrency mechanisms, eg, thread management, synchronizations, or data sharing. Examples of applications coming from multiple domains (eg, financial, medical, and mathematical) and improving their performance through parallel programming design patterns, can be widely found in the literature. [5][6][7] Nevertheless, although all these skeletons aim to simplify the development of parallel applications, there is not a unified standard. 8 Therefore, users require understanding different frameworks, not only to decide which fits best for their purposes, but also to properly use them. Not to mention the migration efforts of applications among frameworks, which becomes as well an arduous task.In order to mitigate this situation, this paper presents GRPPI, a generic and reusable high-level C++ parallel pattern interface that comprises both stream and data-parallel patterns. In general, the goal of

HSTREAM: A Directive-Based Language Extension for Heterogeneous Stream Computing

“…Del Rio Astorga et al [5] and Ernstsson et al [7] propose pattern based high-level application programming interface for stream and data computing on heterogeneous parallel computing systems. While template and skeleton based libraries may be helpful for generic applications, they are not recommended in case programmers want to use data-structures or algorithms optimized for a particular type of problem.…”

“…For instance, source-to-source compilation techniques are proposed to ease programming of data-parallel applications [22,8,1,18]. Similarly, approaches that are based on C++ template library are used for stream and data parallel computing systems [5,7]. Pop and Cohen [16] propose a language extension to OpenMP for stream computing on multi-core architectures.…”

Bringing Parallel Patterns Out of the Corner

“…Some of these frameworks, such as P 3 L [5], ASSIST [57], and SAC [36], provide a new language used to introduce pattern abstractions already in the early phases of the software development process. More recent approaches like FastFlow [22], SkePU [32], GrPPI [26], SPar [37], and PACXX [38], rely on new features of modern C++ language. Patterns are introduced by instantiating class objects at any place in the code or by using suitable C++11 attributes as in SPar.…”

“…Results are validated using three PARSEC benchmarks (Ferret, Dedup, and x264), and they compared their approach to Pthreads and TBB. Other research works evaluated pattern-based programming frameworks by using only microbenchmarks [26,32,44]. In a previous work, we carried out a preliminary evaluation on a small subset of PARSEC applications [21].…”

Algorithms and applications towards the convergence of high‐end data‐intensive and computing systems

“…Dolz et al, 10 in their paper A Generic Parallel Pattern Interface for Stream and Data Processing, propose GRPPI, a generic and reusable parallel pattern interface for both stream processing and data-intensive C++ applications available for high-end nodes. GRPPI accommodates a layer between developers and existing parallel programming back-ends targeting multi-core processors, such as C++ threads, OpenMP and Intel TBB, and accelerators back-end like CUDA Thrust.…”