27 research outputs found
Autotuning Stencil Computations with Structural Ordinal Regression Learning
Stencil computations expose a large and complex space of equivalent implementations. These computations often rely on autotuning techniques, based on iterative compilation or machine learning (ML), to achieve high performance. Iterative compilation autotuning is a challenging and time-consuming task that may be unaffordable in many scenarios. Meanwhile, traditional ML autotuning approaches exploiting classification algorithms (such as neural networks and support vector machines) face difficulties in capturing all features of large search spaces. This paper proposes a new way of automatically tuning stencil computations based on structural learning. By organizing the training data in a set of partially-sorted samples (i.e., rankings), the problem is formulated as a ranking prediction model, which translates to an ordinal regression problem. Our approach can be coupled with an iterative compilation method or used as a standalone autotuner. We demonstrate its potential by comparing it with state-of-the-art iterative compilation methods on a set of nine stencil codes and by analyzing the quality of the obtained ranking in terms of Kendall rank correlation coefficients
Heterogeneous parallel virtual machine: A portable program representation and compiler for performance and energy optimizations on heterogeneous parallel systems
Programming heterogeneous parallel systems, such as the SoCs (System-on-Chip) on mobile and edge devices is extremely difficult; the diverse parallel hardware they contain exposes vastly different hardware instruction sets, parallelism models and memory systems. Moreover, a wide range of diverse hardware and software approximation techniques are available for applications targeting heterogeneous SoCs, further exacerbating the programmability challenges. In this thesis, we alleviate the programmability challenges of such systems using flexible compiler intermediate representation solutions, in order to benefit from the performance and superior energy efficiency of heterogeneous systems.
First, we develop Heterogeneous Parallel Virtual Machine (HPVM), a parallel program representation for heterogeneous systems, designed to enable functional and performance portability across popular parallel hardware. HPVM is based on a hierarchical dataflow graph with side effects. HPVM successfully supports three important capabilities for programming heterogeneous systems: a compiler intermediate representation (IR), a virtual instruction set (ISA), and a basis for runtime scheduling. We use the HPVM representation to implement an HPVM prototype, defining the HPVM IR as an extension of the Low Level Virtual Machine (LLVM) IR. Our results show comparable performance with optimized OpenCL kernels for the target hardware from a single HPVM representation using translators from HPVM virtual ISA to native code, IR optimizations operating directly on the HPVM representation, and the capability for supporting flexible runtime scheduling schemes from a single HPVM representation.
We extend HPVM to ApproxHPVM, introducing hardware-independent approximation metrics in the IR to enable maintaining accuracy information at the IR level and mapping of application-level end-to-end quality metrics to system level "knobs". The approximation metrics quantify the acceptable accuracy loss for individual computations. Application programmers only need to specify high-level, and end-to-end, quality metrics, instead of detailed parameters for individual approximation methods. The ApproxHPVM system then automatically tunes the accuracy requirements of individual computations and maps them to approximate hardware when possible. ApproxHPVM results show significant performance and energy improvements for popular deep learning benchmarks.
Finally, we extend to ApproxHPVM to ApproxTuner, a compiler and runtime system for approximation. ApproxTuner extends ApproxHPVM with a wide range of hardware and software approximation techniques. It uses a three step approximation tuning strategy, a combination of development-time, install-time, and dynamic tuning. Our strategy ensures software portability, even though approximations have highly hardware-dependent performance, and enables efficient dynamic approximation tuning despite the expensive offline steps. ApproxTuner results show significant performance and energy improvements across 7 Deep Neural Networks and 3 image processing benchmarks, and ensures that high-level end-to-end quality specifications are satisfied during adaptive approximation tuning
Machine Learning in Compiler Optimization
In the last decade, machine learning based compilation has moved from an an obscure research niche to a mainstream activity. In this article, we describe the relationship between machine learning and compiler optimisation and introduce the main concepts of features, models, training and deployment. We then provide a comprehensive survey and provide a road map for the wide variety of different research areas. We conclude with a discussion on open issues in the area and potential research directions. This paper provides both an accessible introduction to the fast moving area of machine learning based compilation and a detailed bibliography of its main achievements
AceleraciĂłn de algoritmos de procesamiento de imágenes para el análisis de partĂculas individuales con microscopia electrĂłnica
Tesis Doctoral inĂ©dita cotutelada por la Masaryk University (RepĂşblica Checa) y la Universidad AutĂłnoma de Madrid, Escuela PolitĂ©cnica Superior, Departamento de IngenierĂa Informática. Fecha de Lectura: 24-10-2022Cryogenic Electron Microscopy (Cryo-EM) is a vital field in current structural biology. Unlike X-ray
crystallography and Nuclear Magnetic Resonance, it can be used to analyze membrane proteins and
other samples with overlapping spectral peaks. However, one of the significant limitations of Cryo-EM
is the computational complexity. Modern electron microscopes can produce terabytes of data per single
session, from which hundreds of thousands of particles must be extracted and processed to obtain a
near-atomic resolution of the original sample. Many existing software solutions use high-Performance
Computing (HPC) techniques to bring these computations to the realm of practical usability. The
common approach to acceleration is parallelization of the processing, but in praxis, we face many
complications, such as problem decomposition, data distribution, load scheduling, balancing, and
synchronization. Utilization of various accelerators further complicates the situation, as heterogeneous
hardware brings additional caveats, for example, limited portability, under-utilization due to synchronization,
and sub-optimal code performance due to missing specialization.
This dissertation, structured as a compendium of articles, aims to improve the algorithms used
in Cryo-EM, esp. the SPA (Single Particle Analysis). We focus on the single-node performance
optimizations, using the techniques either available or developed in the HPC field, such as heterogeneous
computing or autotuning, which potentially needs the formulation of novel algorithms. The
secondary goal of the dissertation is to identify the limitations of state-of-the-art HPC techniques. Since
the Cryo-EM pipeline consists of multiple distinct steps targetting different types of data, there is no
single bottleneck to be solved. As such, the presented articles show a holistic approach to performance
optimization.
First, we give details on the GPU acceleration of the specific programs. The achieved speedup is
due to the higher performance of the GPU, adjustments of the original algorithm to it, and application
of the novel algorithms. More specifically, we provide implementation details of programs for movie
alignment, 2D classification, and 3D reconstruction that have been sped up by order of magnitude
compared to their original multi-CPU implementation or sufficiently the be used on-the-fly. In addition
to these three programs, multiple other programs from an actively used, open-source software package
XMIPP have been accelerated and improved.
Second, we discuss our contribution to HPC in the form of autotuning. Autotuning is the ability of
software to adapt to a changing environment, i.e., input or executing hardware. Towards that goal, we
present cuFFTAdvisor, a tool that proposes and, through autotuning, finds the best configuration of the
cuFFT library for given constraints of input size and plan settings. We also introduce a benchmark set
of ten autotunable kernels for important computational problems implemented in OpenCL or CUDA,
together with the introduction of complex dynamic autotuning to the KTT tool.
Third, we propose an image processing framework Umpalumpa, which combines a task-based
runtime system, data-centric architecture, and dynamic autotuning. The proposed framework allows for
writing complex workflows which automatically use available HW resources and adjust to different HW
and data but at the same time are easy to maintainThe project that gave rise to these results received the support of a fellowship from the “la Caixa”
Foundation (ID 100010434). The fellowship code is LCF/BQ/DI18/11660021.
This project has received funding from the European Union’s Horizon 2020 research and innovation
programme under the Marie Skłodowska-Curie grant agreement No. 71367