) project. kubed-sh (pronunciation) lets you execute a program in a Kubernetes cluster without having to
create a container image or learn new commands.node script.js, a Node.js (default version: 9.4) environment is provided and script.js is executed.python script.py, a Python (default version: 3.6) environment is provided and the script.py is executed.ruby script.rb, a Ruby (default version: 2.5) environment is provided and the script.rb is executed.kubed-sh is a proper shell environment, that is, you can expect features such as auto-complete, history operations,
or CTRL+L clearing the screen to work as per usual."In this work we use the GPU porting task for the operative Japanese weather prediction model "ASUCA" as an opportunity to examine productivity issues with OpenACC when applied to structured grid problems. We then propose "Hybrid Fortran", an approach that combines the advantages of directive based methods (no rewrite of existing code necessary) with that of stencil DSLs (memory layout is abstracted). This gives the ability to define multiple parallelizations with different granularities in the same code. Without compromising on performance, this approach enables a major reduction in the code changes required to achieve a hybrid GPU/CPU parallelization – as demonstrated with our ASUCA implementation using Hybrid Fortran.
Massively parallel accelerators such as GPGPUs, manycores and FPGAs represent a powerful and affordable tool for scientists who look to speed up simulations of complex systems. However, porting code to such devices requires a detailed understanding of heterogeneous programming tools and effective strategies for parallelization. In this paper we present a source to source compilation approach with whole-program analysis to automatically transform single-threaded FORTRAN 77 legacy code into OpenCL-accelerated programs with parallelized kernels. The main contributions of our work are: (1) whole-source refactoring to allow any subroutine in the code to be offloaded to an accelerator. (2) Minimization of the data transfer between the host and the accelerator by eliminating redundant transfers. (3) Pragmatic auto-parallelization of the code to be offloaded to the accelerator by identification of parallelizable maps and reductions. We have validated the code transformation performance of the compiler on the NIST FORTRAN 78 test suite and several real-world codes: the Large Eddy Simulator for Urban Flows, a high-resolution turbulent flow model; the shallow water component of the ocean model Gmodel; the Linear Baroclinic Model, an atmospheric climate model and Flexpart-WRF, a particle dispersion simulator. The automatic parallelization component has been tested on as 2-D Shallow Water model (2DSW) and on the Large Eddy Simulator for Urban Flows (UFLES) and produces a complete OpenCL-enabled code base. The fully OpenCL-accelerated versions of the 2DSW and the UFLES are resp. 9x and 20x faster on GPU than the original code on CPU, in both cases this is the same performance as manually ported code.
Given their massively parallel computing capabilities heterogeneous architectures comprised of CPUs and accelerators have been increasingly used to speed-up scientific and engineering applications. Nevertheless, programming such architectures is a challenging task for most non-expert programmers as typical accelerator programming languages (e.g. CUDA and OpenCL) demand a thoroughly understanding of the underlying hardware to enable an effective application speed-up. To achieve that, programmers are usually required to significantly change and adapt program structures and algorithms, thus impacting both performance and productivity. A simpler alternative is to use high-level directive-based programming models like OpenACC and OpenMP. These models allow programmers to insert both directives and runtime calls into existing source code, thus providing hints to the compiler and runtime to perform certain transformations and optimizations on the annotated code regions. In this paper, we present ACLang, an open-source LLVM/Clang compiler framework (http://www.aclang.org) that implements the recently released OpenMP 4.X Accelerator Programming Model. ACLang automatically converts OpenMP 4.X annotated program regions into OpenCL/SPIR kernels, while providing a set of polyhedral based optimizations like tiling and vectorization. OpenCL kernels resulting from ACLang can be executed on any OpenCL/SPIR compatible acceleration device, not only GPUs, but also FPGA accelerators like those found in the Intel HARP architecture. To the best of our knowledge and at the time this paper was written, this is the first LLVM/Clang implementation of the OpenMP 4.X Accelerator Model that provides a source-totarget OpenCL conversion.