High-Level Programming for Many-Cores Using C++14 and the STL

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Detalles Bibliográficos
Publicado en:International Journal of Parallel Programming vol. 46, no. 1 (Feb 2018), p. 23
Autor principal: Haidl, Michael
Otros Autores: Gorlatch, Sergei
Publicado:
Springer Nature B.V.
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Acceso en línea:Citation/Abstract
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Resumen:Programming many-core systems with accelerators (e.g., GPUs) remains a challenging task, even for expert programmers. In the current, low-level approaches—OpenCL and CUDA—two distinct programming models are employed: the host code for the CPU is written in C/C++ with a restricted memory model, while the device code for the accelerator is written using a device-dependent model of CUDA or OpenCL. The programmer is responsible for explicitly specifying parallelism, memory transfers, and synchronization, and also for configuring the program and optimizing its performance for a particular many-core system. This leads to long, poorly structured and error-prone codes, often with a suboptimal performance. We present PACXX—an alternative, unified programming approach for accelerators. In PACXX, both host and device programs are written in the same programming language—the newest C++14 standard with the Standard Template Library (STL), including all modern features: type inference (auto), variadic templates, generic lambda expressions, and the newly proposed parallel extensions of the STL. PACXX includes an easy-to-use and type-safe API for multi-stage programming which allows for aggressive runtime compiler optimizations. We implement PACXX by developing a custom compiler (based on the Clang and LLVM frameworks) and a runtime system, that together perform memory management and synchronization automatically and transparently for the programmer. We evaluate our approach by comparing it to OpenCL regarding program size and target performance.
ISSN:0885-7458
1573-7640
0091-7036
DOI:10.1007/s10766-017-0497-y
Fuente:ABI/INFORM Global