Performance-aware programming models with high productivity. Programming languages explicitly targeted towards HPC that aim to unite The above observations have given rise to a new generation of parallel Tools, standards, and methodologies for effective utilization of Lusk and Yelick : “there exists a critical need for improved software (HPCS), programmer productivity is gradually becoming an issue. Third, as witnessed by the High-Productivity Computing Systems DARPA project Programming straight to the desktop and even to mobile platforms, and therebyĮxposes an increasing number of people to languages and programming paradigms Used in more and more application domains. Supercomputers today are easily accessible over the internet and HPC is being Second, this complexity is becoming a problem for a larger group of people: To an unmanageable source of complexity of HPC algorithms and code. Memory access (NUMA) and non-uniform cluster computing (NUCC).įurther, in order to reach exascale performance, i. e., 10 18 flop/s, ever Programmers thus have to deal with issues such as non-uniform The characteristics of multicore, many-core, GPGPU, accelerators, andĬlusters. Processor connectivity as well as memory architecture. Today, machines have very heterogeneous designs with respect to Programming model is slowly losing momentum:įirst, we observe a continuing trend towards ever more complex hardwareĪrchitectures. Large arrays are divided among processors, and the patterns they use forĬommunication, need to be manually encoded. Way these processes access and manipulate their data, e. g., the way in which Usually extended with MPI for message-passing across parallel processes. ThisĮxplains the continuing dominance of languages such as Fortran, C, or C++ Traditional HPC is based on a programming model in which the programmer is inįull control over the parallel machine in order to maximize performance. Thwart software engineering aspects such as modularity and reusability. As performance and speed are key in HPC, performanceĮngineering aspects such as optimizing a program for data locality often The crossroads of software engineering, algorithms design, mathematics, and High Performance Computing (HPC) is traditionally a field that finds itself at Partitioned Global Address Space Languages. Mattias De Wael, Stefan Marr, Bruno De Fraine, Tom Van Cutsem, Wolfgang De Meuter. Message passing, one-sided communication, data distribution, data access, survey Richer data access cost models remain open challenges.ĭ.3.2 : Concurrent, distributed,Īnd parallel languages D.3.3 :Īdditional Key Words and Phrases: Parallel programming, HPC, PGAS, Our taxonomy reveals that today's PGAS languages focus onĭistributing regular data and distinguish only between local and remote dataĪccess cost, whereas the distribution of irregular data and the adoption of Is introduced, how the address space is partitioned, how data is distributedĪmong the partitions and finally how data is accessed across partitions. Survey proposes a definition and a taxonomy along four axes: how parallelism Today, about a dozen languages exist that adhere to the PGAS model. To this end, PGAS preserves the global address space whileĮmbracing awareness of non-uniform communication costs. Optimizations and to support scalability on large-scale parallelĪrchitectures. Globally shared address space improves productivity, but that a distinctionīetween local and remote data accesses is required to allow performance Model that aims to improve programmer productivity while at the same timeĪiming for high performance. The Partitioned Global Address Space (PGAS) model is a parallel programming
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