pocl: A Performance-Portable OpenCL Implementation

OpenCL is a standard for parallel programming of heterogeneous systems. The benefits of a common programming standard are clear; multiple vendors can provide support for application descriptions written according to the standard, thus reducing the program porting effort. While the standard brings the obvious benefits of platform portability, the performance portability aspects are largely left to the programmer. The situation is made worse due to multiple proprietary vendor implementations with different characteristics, and, thus, required optimization strategies. In this paper, we propose an OpenCL implementation that is both portable and performance portable. At its core is a kernel compiler that can be used to exploit the data parallelism of OpenCL programs on multiple platforms with different parallel hardware styles. The kernel compiler is modularized to perform target-independent parallel region formation separately from the target-specific parallel mapping of the regions to enable support for various styles of fine-grained parallel resources such as subword SIMD extensions, SIMD datapaths and static multi-issue. Unlike previous similar techniques that work on the source level, the parallel region formation retains the information of the data parallelism using the LLVM IR and its metadata infrastructure. This data can be exploited by the later generic compiler passes for efficient parallelization. The proposed open source implementation of OpenCL is also platform portable, enabling OpenCL on a wide range of architectures, both already commercialized and on those that are still under research. The paper describes how the portability of the implementation is achieved. Our results show that most of the benchmarked applications when compiled using pocl were faster or close to as fast as the best proprietary OpenCL implementation for the platform at hand.Comment: This article was published in 2015; it is now openly accessible via arxi

Machine Learning Based Auto-tuning for Enhanced OpenCL Performance Portability

Heterogeneous computing, which combines devices with different architectures, is rising in popularity, and promises increased performance combined with reduced energy consumption. OpenCL has been proposed as a standard for programing such systems, and offers functional portability. It does, however, suffer from poor performance portability, code tuned for one device must be re-tuned to achieve good performance on another device. In this paper, we use machine learning-based auto-tuning to address this problem. Benchmarks are run on a random subset of the entire tuning parameter configuration space, and the results are used to build an artificial neural network based model. The model can then be used to find interesting parts of the parameter space for further search. We evaluate our method with different benchmarks, on several devices, including an Intel i7 3770 CPU, an Nvidia K40 GPU and an AMD Radeon HD 7970 GPU. Our model achieves a mean relative error as low as 6.1%, and is able to find configurations as little as 1.3% worse than the global minimum.Comment: This is a pre-print version an article to be published in the Proceedings of the 2015 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). For personal use onl

A Modeling Approach based on UML/MARTE for GPU Architecture

Nowadays, the High Performance Computing is part of the context of embedded systems. Graphics Processing Units (GPUs) are more and more used in acceleration of the most part of algorithms and applications. Over the past years, not many efforts have been done to describe abstractions of applications in relation to their target architectures. Thus, when developers need to associate applications and GPUs, for example, they find difficulty and prefer using API for these architectures. This paper presents a metamodel extension for MARTE profile and a model for GPU architectures. The main goal is to specify the task and data allocation in the memory hierarchy of these architectures. The results show that this approach will help to generate code for GPUs based on model transformations using Model Driven Engineering (MDE).Comment: Symposium en Architectures nouvelles de machines (SympA'14) (2011

C Language Extensions for Hybrid CPU/GPU Programming with StarPU

Modern platforms used for high-performance computing (HPC) include machines with both general-purpose CPUs, and "accelerators", often in the form of graphical processing units (GPUs). StarPU is a C library to exploit such platforms. It provides users with ways to define "tasks" to be executed on CPUs or GPUs, along with the dependencies among them, and by automatically scheduling them over all the available processing units. In doing so, it also relieves programmers from the need to know the underlying architecture details: it adapts to the available CPUs and GPUs, and automatically transfers data between main memory and GPUs as needed. While StarPU's approach is successful at addressing run-time scheduling issues, being a C library makes for a poor and error-prone programming interface. This paper presents an effort started in 2011 to promote some of the concepts exported by the library as C language constructs, by means of an extension of the GCC compiler suite. Our main contribution is the design and implementation of language extensions that map to StarPU's task programming paradigm. We argue that the proposed extensions make it easier to get started with StarPU,eliminate errors that can occur when using the C library, and help diagnose possible mistakes. We conclude on future work

CU2CL: A CUDA-to-OpenCL Translator for Multi- and Many-core Architectures

The use of graphics processing units (GPUs) in high-performance parallel computing continues to become more prevalent, often as part of a heterogeneous system. For years, CUDA has been the de facto programming environment for nearly all general-purpose GPU (GPGPU) applications. In spite of this, the framework is available only on NVIDIA GPUs, traditionally requiring reimplementation in other frameworks in order to utilize additional multi- or many-core devices. On the other hand, OpenCL provides an open and vendorneutral programming environment and runtime system. With implementations available for CPUs, GPUs, and other types of accelerators, OpenCL therefore holds the promise of a “write once, run anywhere” ecosystem for heterogeneous computing. Given the many similarities between CUDA and OpenCL, manually porting a CUDA application to OpenCL is typically straightforward, albeit tedious and error-prone. In response to this issue, we created CU2CL, an automated CUDA-to- OpenCL source-to-source translator that possesses a novel design and clever reuse of the Clang compiler framework. Currently, the CU2CL translator covers the primary constructs found in CUDA runtime API, and we have successfully translated many applications from the CUDA SDK and Rodinia benchmark suite. The performance of our automatically translated applications via CU2CL is on par with their manually ported countparts

TANGO: Transparent heterogeneous hardware Architecture deployment for eNergy Gain in Operation

The paper is concerned with the issue of how software systems actually use Heterogeneous Parallel Architectures (HPAs), with the goal of optimizing power consumption on these resources. It argues the need for novel methods and tools to support software developers aiming to optimise power consumption resulting from designing, developing, deploying and running software on HPAs, while maintaining other quality aspects of software to adequate and agreed levels. To do so, a reference architecture to support energy efficiency at application construction, deployment, and operation is discussed, as well as its implementation and evaluation plans.Comment: Part of the Program Transformation for Programmability in Heterogeneous Architectures (PROHA) workshop, Barcelona, Spain, 12th March 2016, 7 pages, LaTeX, 3 PNG figure