A GPU acceleration of 3-D Fourier reconstruction in cryo-EM

2019 ◽  
Vol 33 (5) ◽  
pp. 948-959 ◽  
Author(s):  
David Střelák ◽  
Carlos Óscar S Sorzano ◽  
José María Carazo ◽  
Jiří Filipovič
Keyword(s):  
Graphics Processing Unit ◽  
Internal Symmetry ◽  
Processing Unit ◽  
Volume Reconstruction ◽  
Optimization Parameters ◽  
Fourier Reconstruction ◽  
Race Conditions ◽  
Graphics Processing ◽  
Gpu Architecture ◽  
Memory Pattern

Cryo-electron microscopy is a popular method for macromolecules structure determination. Reconstruction of a 3-D volume from raw data obtained from a microscope is highly computationally demanding. Thus, acceleration of the reconstruction has a great practical value. In this article, we introduce a novel graphics processing unit (GPU)-friendly algorithm for direct Fourier reconstruction, one of the main computational bottlenecks in the 3-D volume reconstruction pipeline for some experimental cases (particularly those with a large number of images and a high internal symmetry). Contrary to the state of the art, our algorithm uses a gather memory pattern, improving cache locality and removing race conditions in parallel writing into the 3-D volume. We also introduce a finely tuned CUDA implementation of our algorithm, using auto-tuning to search for a combination of optimization parameters maximizing performance on a given GPU architecture. Our CUDA implementation is integrated in widely used software Xmipp, version 3.19, reaching 11.4× speedup compared to the original parallel CPU implementation using GPU with comparable power consumption. Moreover, we have reached 31.7× speedup using four GPUs and 2.14×–5.96× speedup compared to optimized GPU implementation based on a scatter memory pattern.

Scalable Graphics Processing Unit–Based Multiscale Linear Solvers for Reservoir Simulation

SPE Journal ◽  
2021 ◽  
pp. 1-20
Author(s):  
A. M. Manea ◽  
T. Almani
Keyword(s):  
Shared Memory ◽  
Reservoir Simulation ◽  
Graphics Processing Unit ◽  
Parallel Architecture ◽  
Multiscale Methods ◽  
Massively Parallel ◽  
Processing Unit ◽  
Multicore Architecture ◽  
Graphics Processing ◽  
Gpu Architecture

Summary In this work, the scalability of two key multiscale solvers for the pressure equation arising from incompressible flow in heterogeneous porous media, namely, the multiscale finite volume (MSFV) solver, and the restriction-smoothed basis multiscale (MsRSB) solver, are investigated on the graphics processing unit (GPU) massively parallel architecture. The robustness and scalability of both solvers are compared against their corresponding carefully optimized implementation on the shared-memory multicore architecture in a structured problem setting. Although several components in MSFV and MsRSB algorithms are directly parallelizable, their scalability on the GPU architecture depends heavily on the underlying algorithmic details and data-structure design of every step, where one needs to ensure favorable control and data flow on the GPU, while extracting enough parallel work for a massively parallel environment. In addition, the type of algorithm chosen for each step greatly influences the overall robustness of the solver. Thus, we extend the work on the parallel multiscale methods of Manea et al. (2016) to map the MSFV and MsRSB special kernels to the massively parallel GPU architecture. The scalability of our optimized parallel MSFV and MsRSB GPU implementations are demonstrated using highly heterogeneous structured 3D problems derived from the SPE10 Benchmark (Christie and Blunt 2001). Those problems range in size from millions to tens of millions of cells. For both solvers, the multicore implementations are benchmarked on a shared-memory multicore architecture consisting of two packages of Intel® Cascade Lake Xeon Gold 6246 central processing unit (CPU), whereas the GPU implementations are benchmarked on a massively parallel architecture consisting of NVIDIA Volta V100 GPUs. We compare the multicore implementations to the GPU implementations for both the setup and solution stages. Finally, we compare the parallel MsRSB scalability to the scalability of MSFV on the multicore (Manea et al. 2016) and GPU architectures. To the best of our knowledge, this is the first parallel implementation and demonstration of these versatile multiscale solvers on the GPU architecture. NOTE: This paper is published as part of the 2021 SPE Reservoir Simulation Conference Special Issue.

HIGH PRECISION INTEGER ADDITION, SUBTRACTION AND MULTIPLICATION WITH A GRAPHICS PROCESSING UNIT

2010 ◽  
Vol 20 (04) ◽  
pp. 293-306 ◽  
Author(s):  
NIALL EMMART ◽  
CHARLES WEEMS
Keyword(s):  
High Precision ◽  
Graphics Processing Unit ◽  
Divide And Conquer ◽  
Processing Unit ◽  
Good Potential ◽  
Addition And Subtraction ◽  
Integer Multiplication ◽  
On Chip ◽  
Graphics Processing ◽  
Gpu Architecture

In this paper we evaluate the potential for using an NVIDIA graphics processing unit (GPU) to accelerate high precision integer multiplication, addition, and subtraction. The reported peak vector performance for a typical GPU appears to offer good potential for accelerating such a computation. Because of limitations in the on-chip memory, the high cost of kernel launches, and the nature of the architecture's support for parallelism, we used a hybrid algorithmic approach to obtain good performance on multiplication. On the GPU itself we adapt the Strassen FFT algorithm to multiply 32KB chunks, while on the CPU we adapt the Karatsuba divide-and-conquer approach to optimize application of the GPU's partial multiplies, which are viewed as "digits" by our implementation of Karatsuba. Even with this approach, the result is at best a factor of three increase in performance, compared with using the GMP package on a 64-bit CPU at a comparable technology node. Our implementations of addition and subtraction achieve up to a factor of eight improvement. We identify the issues that limit performance and discuss the likely impact of planned advances in GPU architecture.

scholarly journals On the issue of fuzzy timing estimations of the algorithms running at GPU and CPU architectures

2019 ◽  
Vol 135 ◽  
pp. 01082 ◽  
Author(s):  
Oleg Agibalov ◽  
Nikolay Ventsov
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Running Time ◽  
Average Value ◽  
Central Processing ◽  
Graphics Processing ◽  
Number Of Individuals ◽  
Gpu Architecture

We consider the task of comparing fuzzy estimates of the execution parameters of genetic algorithms implemented at GPU (graphics processing unit’ GPU) and CPU (central processing unit) architectures. Fuzzy estimates are calculated based on the averaged dependencies of the genetic algorithms running time at GPU and CPU architectures from the number of individuals in the populations processed by the algorithm. The analysis of the averaged dependences of the genetic algorithms running time at GPU and CPU-architectures showed that it is possible to process 10’000 chromosomes at GPU-architecture or 5’000 chromosomes at CPUarchitecture by genetic algorithm in approximately 2’500 ms. The following is correct for the cases under consideration: “Genetic algorithms (GA) are performed in approximately 2, 500 ms (on average), ” and a sections of fuzzy sets, with a = 0.5, correspond to the intervals [2, 000.2399] for GA performed at the GPU-architecture, and [1, 400.1799] for GA performed at the CPU-architecture. Thereby, it can be said that in this case, the actual execution time of the algorithm at the GPU architecture deviates in a lesser extent from the average value than at the CPU.

Efficient Prefix Scan for the GPU-Based Implementation of Random Forest

2015 ◽  
pp. 140-151
Author(s):  
Bojan Novak
Keyword(s):  
Random Forest ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Random Forest Algorithm ◽  
Central Processing ◽  
Split Point ◽  
Parallel Scan ◽  
Graphics Processing ◽  
Gpu Architecture ◽  
Gpu Implementation

The random forest ensemble learning with the Graphics Processing Unit (GPU) version of prefix scan method is presented. The efficiency of the implementation of the random forest algorithm depends critically on the scan (prefix sum) algorithm. The prefix scan is used in the depth-first implementation of optimal split point computation. Described are different implementations of the prefix scan algorithms. The speeds of the algorithms depend on three factors: the algorithm itself, which could be improved, the programming skills, and the compiler. In parallel environments, things are even more complicated and depend on the programmer´s knowledge of the Central Processing Unit (CPU) or the GPU architecture. An efficient parallel scan algorithm that avoids bank conflicts is crucial for the prefix scan implementation. In our tests, multicore CPU and GPU implementation based on NVIDIA´s CUDA is compared.

History and Evolution of GPU Architecture

2016 ◽  
pp. 109-135
Author(s):  
Prashanta Kumar Das ◽  
Ganesh Chandra Deka
Keyword(s):  
Image Processing ◽  
Graphics Processing Unit ◽  
Hardware Architecture ◽  
Central Processing Unit ◽  
Processing Unit ◽  
3D Image ◽  
Central Processing ◽  
Graphics Processing ◽  
Graphics Engine ◽  
Gpu Architecture

The Graphics Processing Unit (GPU) is a specialized and highly parallel microprocessor designed to offload 2D/3D image from the Central Processing Unit (CPU) to expedite image processing. The modern GPU is not only a powerful graphics engine, but also a parallel programmable processor with high precision and powerful features. It is forcasted that by 2020, 48 Core GPU will be available while by 2030 GPU with 3000 core is likely to be available.This chapter describes the chronology of evolution of GPU hardware architecture and the future ahead.

Implementing wide baseline matching algorithms on a graphics processing unit.

2007 ◽  
Author(s):  
Fredrick H. Rothganger ◽  
Kurt W. Larson ◽  
Antonio Ignacio Gonzales ◽  
Daniel S. Myers
Keyword(s):  
Graphics Processing Unit ◽  
Processing Unit ◽  
Wide Baseline Matching ◽  
Graphics Processing

scholarly journals Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

2021 ◽  
Vol 22 (10) ◽  
pp. 5212
Author(s):  
Andrzej Bak
Keyword(s):  
Molecular Conformation ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Diverse Range ◽  
Current State ◽  
Gpu Clusters ◽  
Pharmacophore Hypothesis ◽  
Rising Power ◽  
Graphics Processing ◽  
Ligand Conformation

A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.

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