PolyMage

Mullapudi, Ravi Teja; Vasista, Vinay; Bondhugula, Uday

doi:10.1145/2786763.2694364

Cited by 64 publications

(17 citation statements)

References 50 publications

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“…Programming such an optimization manually is tedious and virtually impossible for large-scale codes. Traditional compilers and Polyhedral compilers [32,33,34,35,36,37,38] have been targeting this problem for many years, yet they are not applicable to problems of this size. Therefore in OPS we have implemented a feature that determines the crossloop dependencies and constructs execution schedules at runtime [24].…”

Section: Tilingmentioning

confidence: 99%

Large-scale performance of a DSL-based multi-block structured-mesh application for Direct Numerical Simulation

Mudalige

Reguly

Jammy

et al. 2019

Journal of Parallel and Distributed Computing

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SBLI (Shock-wave/Boundary-layer Interaction) is a large-scale Computational Fluid Dynamics(CFD) application, developed over 20 years at the University of Southampton and extensively used within the UK Turbulence Consortium. It is capable of performing Direct Numerical Simulations (DNS) or Large Eddy Simulation (LES) of shock-wave/boundarylayer interaction problems over highly detailed multi-block structured mesh geometries. SBLI presents major challenges in data organization and movement that need to be overcome for continued high performance on emerging massively parallel hardware platforms. In this paper we present research in achieving this goal through the OPS embedded domainspecific language. OPS targets the domain of multi-block structured mesh applications. It provides an API embedded in C/C++ and Fortran and makes use of automatic code generation and compilation to produce executables capable of running on a range of parallel hardware systems. The core functionality of SBLI is captured using a new framework called OpenSBLI which enables a developer to declare the partial differential equations using Einstein notation and then automatically carryout discretization and generation of OPS (C/C++) API code. OPS is then used to automatically generate a wide range of parallel implementations. Using this multi-layered abstractions approach we demonstrate how new opportunities for further optimizations can be gained, such as fine-tuning the computation intensity and reducing data movement and apply them automatically. Performance results demonstrate there is no performance loss due to the high-level development strategy with OPS and OpenSBLI, with performance matching or exceeding the hand-tuned original code on all CPU nodes tested. The data movement optimizations provide over 3× speedups on CPU nodes, while GPUs provide 5× speedups over the best performing CPU node. The OPS generated parallel code also demonstrates excellent scalability on nearly 100K cores on a Cray XC30 (ARCHER at EPCC) and on over 4K GPUs on a CrayXK7 (Titan at ORNL).

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Section: Tilingmentioning

confidence: 99%

Large-scale performance of a DSL-based multi-block structured-mesh application for Direct Numerical Simulation

Mudalige

Reguly

Jammy

et al. 2019

Journal of Parallel and Distributed Computing

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“…As the SDFG provides general-purpose state machines with dataflow, all the above models can be fully represented within it, where SDFGs have the added benefit of encapsulating fine-grained data dependencies. [18,44,51,58,60,63] provide a fixed set of high-level program transformations, similar to those presented on SDFGs. In particular, Halide's schedules are by definition data-centric, and the same applies to polyhedral loop transformations in CHiLL.…”

Section: Related Workmentioning

confidence: 99%

“…The checks ensure that the array is indeed transient and not used in other instances of data access nodes. To avoid recomputing subsets (which may not be feasible to compute symbolically), if the transformation operates in strict mode, it only matches two arrays of the same shape (lines [51][52][53][54][55][56]. The transformation then operates in a straightforward manner, renaming the memlets to point to the second (not removed) array (lines 66-70) and redirecting dataflow edges to that data access node (lines 73-74).…”

Section: Polybench Flagsmentioning

confidence: 99%

Stateful dataflow multigraphs

Ben-Nun

Licht

Ziogas

et al. 2019

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

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The ubiquity of accelerators in high-performance computing has driven programming complexity beyond the skill-set of the average domain scientist. To maintain performance portability in the future, it is imperative to decouple architecture-specific programming paradigms from the underlying scientific computations. We present the Stateful DataFlow multiGraph (SDFG), a data-centric intermediate representation that enables separating program definition from its optimization. By combining fine-grained data dependencies with high-level control-flow, SDFGs are both expressive and amenable to program transformations, such as tiling and double-buffering. These transformations are applied to the SDFG in an interactive process, using extensible pattern matching, graph rewriting, and a graphical user interface. We demonstrate SDFGs on CPUs, GPUs, and FPGAs over various motifs -from fundamental computational kernels to graph analytics. We show that SDFGs deliver competitive performance, allowing domain scientists to develop applications naturally and port them to approach peak hardware performance without modifying the original scientific code.HPC programmers have long sacrificed ease of programming and portability for achieving better performance. This mindset was established at a time when computer nodes had a single processor/core and were programmed with C/Fortran and MPI. The last decade, witnessing the end of Dennard scaling and Moore's law, brought a flurry of new technologies into the compute nodes. Those range from simple multi-core and manycore CPUs to heterogeneous GPUs and specialized FPGAs. To support those architectures, the complexity of OpenMP's specification grew by more than an order of magnitude from 63 pages in OpenMP 1.0 to 666 pages in OpenMP 5.0. This one example illustrates how (performance) programming complexity shifted from network scalability to node

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“…However, it typically requires an initial loop nest to be provided by the programmer, whereas a DSL like Halide can describe the functionality at an abstract level. PolyMage [Mullapudi et al 2015] is another DSL which offers such an abstract description style. It automatically generates optimized loop nests using polyhedral optimization techniques.…”

Section: Heterogeneous Platform Programmabilitymentioning

confidence: 99%

Extending Halide to Improve Software Development for Imaging DSPs

Vocke

Corporaal

Jordans

et al. 2017

ACM Trans. Archit. Code Optim.

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MASTER Extending halide to improve software development for imaging DSPsVocke, S. Award date: 2016Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration. General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Specialized Digital Signal Processors (DSPs), which can be found in a wide range of modern devices, play an important role in power-efficient, high-performance image processing. Applications including camera sensor post-processing and computer vision can benefit from being (partially) mapped onto such DSPs. However, due to their specialized instruction sets and dependence on low-level code optimization, developing applications for DSPs is often more time-consuming and error-prone than for general-purpose processors. Halide is a domain-specific language (DSL) which enables low-effort development of portable, highperformance imaging pipelines -a combination of qualities which is currently hard, if not impossible to find among DSP programming models. I propose a set of extensions and modifications to Halide in order to support DSPs in combination with arbitrary C compilers, including a template solution to support diverse target instruction sets and heterogeneous scratchpad memories. Using a commercial Intel DSP, I demonstrate that this solution can be used to achieve performance comparable to tuned C code, while leading to a reduction in development time and code complexity. The results also show that DSPs are attractive alternatives to CPUs and GPUs for power-and area-efficient image processing using Halide. Categories and Subject Descriptors: [] ACM Reference Format:

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PolyMage

Cited by 64 publications

References 50 publications

Large-scale performance of a DSL-based multi-block structured-mesh application for Direct Numerical Simulation

Large-scale performance of a DSL-based multi-block structured-mesh application for Direct Numerical Simulation

Stateful dataflow multigraphs

Extending Halide to Improve Software Development for Imaging DSPs

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