Parallel accelerators such as GPUs are notoriously hard to program; exploiting their full performance potential is a job best left for ninja programmers. High-level programming languages coupled with optimizing compilers have been proposed to attempt to address this issue. However, they rely on device-specific heuristics or hard-coded library implementations to achieve good performance resulting in non-portable solutions that need to be re-optimized for every new device.Achieving performance portability is the holy grail of high-performance computing and has so far remained an open problem even for well studied applications like matrix multiplication. We argue that what is needed is a way to describe applications at a high-level without committing to particular implementations. To this end, we developed in a previous paper a functional data-parallel language which allows applications to be expressed in a device neutral way. We use a set of well-defined rewrite rules to automatically transform programs into semantically equivalent devicespecific forms, from which OpenCL code is generated.In this paper, we demonstrate how this approach produces high-performance OpenCL code for GPUs with a wellstudied, well-understood application: matrix multiplication. Starting from a single high-level program, our compiler automatically generate highly optimized and specialized implementations. We group simple rewrite rules into more complex macro-rules, each describing a well-known optimization like tiling and register blocking in a composable way. Using an exploration strategy our compiler automatically generates 50,000 OpenCL kernels, each providing a differently optimized -but provably correct -implementation of matrix multiplication. The automatically generated code offers competitive performance compared to the manually tuned MAGMA library implementations of matrix multiplication on Nvidia and even outperforms AMD's clBLAS library.
State of the art automatic optimization of OpenCL applications focuses on improving the performance of individual compute kernels. Programmers address opportunities for inter-kernel optimization in specific applications by ad-hoc hand tuning: manually fusing kernels together. However, the complexity of interactions between host and kernel code makes this approach weak or even unviable for applications involving more than a small number of kernel invocations or a highly dynamic control flow, leaving substantial potential opportunities unexplored. It also leads to an over complex, hard to maintain code base.We present Helium, a transparent OpenCL overlay which discovers, manipulates and exploits opportunities for inter-and intrakernel optimization. Helium is implemented as preloaded library and uses a delay-optimize-replay mechanism in which kernel calls are intercepted, collectively optimized, and then executed according to an improved execution plan. This allows us to benefit from composite optimizations, on large, dynamically complex applications, with no impact on the code base. Our results show that Helium obtains at least the same, and frequently even better performance, than carefully handtuned code. Helium outperforms handoptimized code where the exact dynamic composition of compute kernel cannot be known statically. In these cases, we demonstrate speedups of up to 3x over unoptimized code and an average speedup of 1.4x over hand optimized code.
GPGPUs are a powerful and energy-efficient solution for many problems. For higher performance or larger problems, it is necessary to distribute the problem across multiple GPUs, increasing the already high programming complexity.In this article, we focus on abstracting the complexity of multi-GPU programming for stencil computation. We show that the best strategy depends not only on the stencil operator, problem size, and GPU, but also on the PCI express layout. This adds nonuniform characteristics to a seemingly homogeneous setup, causing up to 23% performance loss. We address this issue with an autotuner that optimizes the distribution across multiple GPUs.
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