1 research outputs found
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