arXiv subjects

George Bisbas

Publications and source records attributed to George Bisbas.

5 recordsLinked to original sources

arXiv2026

An MLIR Lowering Pipeline for Stencils at Wafer-Scale

The Cerebras Wafer-Scale Engine (WSE) delivers performance at an unprecedented scale of over 900,000 compute units, all connected via a single-wafer on-chip interconnect. Initially designed for AI, the WSE architecture is also well-suited for High Performance Computing (HPC). However, its distributed asynchronous programming model diverges significantly from the simple sequential or bulk-synchronous programs that one would typically derive for a given mathematical program description. Targeting the WSE requires a bespoke re-implementation when porting existing code. The absence of WSE support in compilers such as MLIR, meant that there was little hope for automating this process. Stencils are ubiquitous in HPC, and in this paper we explore the hypothesis that domain specific information about stencils can be leveraged by the compiler to automatically target the WSE without requiring application-level code changes. We present a compiler pipeline that transforms stencil-based kernels into highly optimized CSL code for the WSE, bridging the semantic gap between the mathematical representation of the problem and the WSE's asynchronous execution model. Based upon five benchmarks across three HPC programming technologies, running on both the Cerebras WSE2 and WSE3, our approach delivers comparable, if not slightly better, performance than manually optimized code. Furthermore, without requiring any application level code changes, performance on the WSE3 is around 14 times faster than 128 Nvidia A100 GPUs and 20 times faster than 128 nodes of a CPU-based Cray-EX supercomputer when using our approach.

cs.DC↗

arXiv2024

A shared compilation stack for distributed-memory parallelism in stencil DSLs

Domain Specific Languages (DSLs) increase programmer productivity and provide high performance. Their targeted abstractions allow scientists to express problems at a high level, providing rich details that optimizing compilers can exploit to target current- and next-generation supercomputers. The convenience and performance of DSLs come with significant development and maintenance costs. The siloed design of DSL compilers and the resulting inability to benefit from shared infrastructure cause uncertainties around longevity and the adoption of DSLs at scale. By tailoring the broadly-adopted MLIR compiler framework to HPC, we bring the same synergies that the machine learning community already exploits across their DSLs (e.g. Tensorflow, PyTorch) to the finite-difference stencil HPC community. We introduce new HPC-specific abstractions for message passing targeting distributed stencil computations. We demonstrate the sharing of common components across three distinct HPC stencil-DSL compilers: Devito, PSyclone, and the Open Earth Compiler, showing that our framework generates high-performance executables based upon a shared compiler ecosystem.

cs.DC↗

arXiv2023

Automated MPI-X code generation for scalable finite-difference solvers

Partial differential equations (PDEs) are crucial in modeling diverse phenomena across scientific disciplines, including seismic and medical imaging, computational fluid dynamics, image processing, and neural networks. Solving these PDEs at scale is an intricate and time-intensive process that demands careful tuning. This paper introduces automated code-generation techniques specifically tailored for distributed memory parallelism (DMP) to execute explicit finite-difference (FD) stencils at scale, a fundamental challenge in numerous scientific applications. These techniques are implemented and integrated into the Devito DSL and compiler framework, a well-established solution for automating the generation of FD solvers based on a high-level symbolic math input. Users benefit from modeling simulations for real-world applications at a high-level symbolic abstraction and effortlessly harnessing HPC-ready distributed-memory parallelism without altering their source code. This results in drastic reductions both in execution time and developer effort. A comprehensive performance evaluation of Devito's DMP via MPI demonstrates highly competitive strong and weak scaling on CPU and GPU clusters, proving its effectiveness and capability to meet the demands of large-scale scientific simulations.

cs.DC↗

arXiv2020

Temporal blocking of finite-difference stencil operators with sparse "off-the-grid" sources

Stencil kernels dominate a range of scientific applications, including seismic and medical imaging, image processing, and neural networks. Temporal blocking is a performance optimization that aims to reduce the required memory bandwidth of stencil computations by re-using data from the cache for multiple time steps. It has already been shown to be beneficial for this class of algorithms. However, applying temporal blocking to practical applications' stencils remains challenging. These computations often consist of sparsely located operators not aligned with the computational grid ("off-the-grid"). Our work is motivated by modeling problems in which source injections result in wavefields that must then be measured at receivers by interpolation from the grided wavefield. The resulting data dependencies make the adoption of temporal blocking much more challenging. We propose a methodology to inspect these data dependencies and reorder the computation, leading to performance gains in stencil codes where temporal blocking has not been applicable. We implement this novel scheme in the Devito domain-specific compiler toolchain. Devito implements a domain-specific language embedded in Python to generate optimized partial differential equation solvers using the finite-difference method from high-level symbolic problem definitions. We evaluate our scheme using isotropic acoustic, anisotropic acoustic, and isotropic elastic wave propagators of industrial significance. After auto-tuning, performance evaluation shows that this enables substantial performance improvement through temporal blocking over highly-optimized vectorized spatially-blocked code of up to 1.6x.

cs.DC↗

arXiv2020

Scaling through abstractions -- high-performance vectorial wave simulations for seismic inversion with Devito

[Devito] is an open-source Python project based on domain-specific language and compiler technology. Driven by the requirements of rapid HPC applications development in exploration seismology, the language and compiler have evolved significantly since inception. Sophisticated boundary conditions, tensor contractions, sparse operations and features such as staggered grids and sub-domains are all supported; operators of essentially arbitrary complexity can be generated. To accommodate this flexibility whilst ensuring performance, data dependency analysis is utilized to schedule loops and detect computational-properties such as parallelism. In this article, the generation and simulation of MPI-parallel propagators (along with their adjoints) for the pseudo-acoustic wave-equation in tilted transverse isotropic media and the elastic wave-equation are presented. Simulations are carried out on industry scale synthetic models in a HPC Cloud system and reach a performance of 28TFLOP/s, hence demonstrating Devito's suitability for production-grade seismic inversion problems.

physics.comp-ph↗