Browse
Submit Data
Databases
API
Help

Dataset Information

10 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Performance data of multiple-precision scalar and vector BLAS operations on CPU and GPU.

ABSTRACT: Many optimized linear algebra packages support the single- and double-precision floating-point data types. However, there are a number of important applications that require a higher level of precision, up to hundreds or even thousands of digits. This article presents performance data of four dense basic linear algebra subprograms - ASUM, DOT, SCAL, and AXPY - implemented using existing extended-/multiple-precision software for conventional central processing units and CUDA compatible graphics processing units. The following open source packages are considered: MPFR, MPDECIMAL, ARPREC, MPACK, XBLAS, GARPREC, CAMPARY, CUMP, and MPRES-BLAS. The execution time of CPU and GPU implementations is measured at a fixed problem size and various levels of numeric precision. The data in this article are related to the research article entitled "Design and implementation of multiple-precision BLAS Level 1 functions for graphics processing units" [1].

SUBMITTER: Isupov K

PROVIDER: S-EPMC7195515 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Performance data of multiple-precision scalar and vector BLAS operations on CPU and GPU.

Isupov Konstantin K

Data in brief 20200421

Many optimized linear algebra packages support the single- and double-precision floating-point data types. However, there are a number of important applications that require a higher level of precision, up to hundreds or even thousands of digits. This article presents performance data of four dense basic linear algebra subprograms - ASUM, DOT, SCAL, and AXPY - implemented using existing extended-/multiple-precision software for conventional central processing units and CUDA compatible graphics p ...[more]

PMID: 32373682

Similar Datasets

Tempest: GPU-CPU computing for high-throughput database spectral matching.

Project description:Modern mass spectrometers are now capable of producing hundreds of thousands of tandem (MS/MS) spectra per experiment, making the translation of these fragmentation spectra into peptide matches a common bottleneck in proteomics research. When coupled with experimental designs that enrich for post-translational modifications such as phosphorylation and/or include isotopically labeled amino acids for quantification, additional burdens are placed on this computational infrastructure by shotgun sequencing. To address this issue, we have developed a new database searching program that utilizes the massively parallel compute capabilities of a graphical processing unit (GPU) to produce peptide spectral matches in a very high throughput fashion. Our program, named Tempest, combines efficient database digestion and MS/MS spectral indexing on a CPU with fast similarity scoring on a GPU. In our implementation, the entire similarity score, including the generation of full theoretical peptide candidate fragmentation spectra and its comparison to experimental spectra, is conducted on the GPU. Although Tempest uses the classical SEQUEST XCorr score as a primary metric for evaluating similarity for spectra collected at unit resolution, we have developed a new "Accelerated Score" for MS/MS spectra collected at high resolution that is based on a computationally inexpensive dot product but exhibits scoring accuracy similar to that of the classical XCorr. In our experience, Tempest provides compute-cluster level performance in an affordable desktop computer.

| S-EPMC3397144 | biostudies-literature

pyPaSWAS: Python-based multi-core CPU and GPU sequence alignment.

Project description:Our previously published CUDA-only application PaSWAS for Smith-Waterman (SW) sequence alignment of any type of sequence on NVIDIA-based GPUs is platform-specific and therefore adopted less than could be. The OpenCL language is supported more widely and allows use on a variety of hardware platforms. Moreover, there is a need to promote the adoption of parallel computing in bioinformatics by making its use and extension more simple through more and better application of high-level languages commonly used in bioinformatics, such as Python.The novel application pyPaSWAS presents the parallel SW sequence alignment code fully packed in Python. It is a generic SW implementation running on several hardware platforms with multi-core systems and/or GPUs that provides accurate sequence alignments that also can be inspected for alignment details. Additionally, pyPaSWAS support the affine gap penalty. Python libraries are used for automated system configuration, I/O and logging. This way, the Python environment will stimulate further extension and use of pyPaSWAS.pyPaSWAS presents an easy Python-based environment for accurate and retrievable parallel SW sequence alignments on GPUs and multi-core systems. The strategy of integrating Python with high-performance parallel compute languages to create a developer- and user-friendly environment should be considered for other computationally intensive bioinformatics algorithms.

| S-EPMC5749749 | biostudies-literature

Scalable molecular dynamics on CPU and GPU architectures with NAMD.

Project description:NAMDis a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and GPU-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity clusters commonly found in academic environments. It is written in C++ and leans on Charm++ parallel objects for optimal performance on low-latency architectures. NAMD is a versatile, multipurpose code that gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe the underlying concepts utilized by NAMD and their implementation, most notably for handling long-range electrostatics; controlling the temperature, pressure, and pH; applying external potentials on tailored grids; leveraging massively parallel resources in multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchemical or geometrical transformations and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts toward achieving optimal performance on GPU-based architectures, for pushing back the limitations that have prevented biologically realistic billion-atom objects to be fruitfully simulated, and for making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD is distributed free of charge with its source code at www.ks.uiuc.edu.

| S-EPMC7395834 | biostudies-literature

MOIL-opt: Energy-Conserving Molecular Dynamics on a GPU/CPU system.

Project description:We report an optimized version of the molecular dynamics program MOIL that runs on a shared memory system with OpenMP and exploits the power of a Graphics Processing Unit (GPU). The model is of heterogeneous computing system on a single node with several cores sharing the same memory and a GPU. This is a typical laboratory tool, which provides excellent performance at minimal cost. Besides performance, emphasis is made on accuracy and stability of the algorithm probed by energy conservation for explicit-solvent atomically-detailed-models. Especially for long simulations energy conservation is critical due to the phenomenon known as "energy drift" in which energy errors accumulate linearly as a function of simulation time. To achieve long time dynamics with acceptable accuracy the drift must be particularly small. We identify several means of controlling long-time numerical accuracy while maintaining excellent speedup. To maintain a high level of energy conservation SHAKE and the Ewald reciprocal summation are run in double precision. Double precision summation of real-space non-bonded interactions improves energy conservation. In our best option, the energy drift using 1fs for a time step while constraining the distances of all bonds, is undetectable in 10ns simulation of solvated DHFR (Dihydrofolate reductase). Faster options, shaking only bonds with hydrogen atoms, are also very well behaved and have drifts of less than 1kcal/mol per nanosecond of the same system. CPU/GPU implementations require changes in programming models. We consider the use of a list of neighbors and quadratic versus linear interpolation in lookup tables of different sizes. Quadratic interpolation with a smaller number of grid points is faster than linear lookup tables (with finer representation) without loss of accuracy. Atomic neighbor lists were found most efficient. Typical speedups are about a factor of 10 compared to a single-core single-precision code.

| S-EPMC3274753 | biostudies-literature

Multi-core CPU or GPU-accelerated Multiscale Modeling for Biomolecular Complexes.

Project description:Multi-scale modeling plays an important role in understanding the structure and biological functionalities of large biomolecular complexes. In this paper, we present an efficient computational framework to construct multi-scale models from atomic resolution data in the Protein Data Bank (PDB), which is accelerated by multi-core CPU and programmable Graphics Processing Units (GPU). A multi-level summation of Gaus-sian kernel functions is employed to generate implicit models for biomolecules. The coefficients in the summation are designed as functions of the structure indices, which specify the structures at a certain level and enable a local resolution control on the biomolecular surface. A method called neighboring search is adopted to locate the grid points close to the expected biomolecular surface, and reduce the number of grids to be analyzed. For a specific grid point, a KD-tree or bounding volume hierarchy is applied to search for the atoms contributing to its density computation, and faraway atoms are ignored due to the decay of Gaussian kernel functions. In addition to density map construction, three modes are also employed and compared during mesh generation and quality improvement to generate high quality tetrahedral meshes: CPU sequential, multi-core CPU parallel and GPU parallel. We have applied our algorithm to several large proteins and obtained good results.

| S-EPMC3858848 | biostudies-literature

GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores.

Project description:BACKGROUND: Gene-gene interaction in genetic association studies is computationally intensive when a large number of SNPs are involved. Most of the latest Central Processing Units (CPUs) have multiple cores, whereas Graphics Processing Units (GPUs) also have hundreds of cores and have been recently used to implement faster scientific software. However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic interaction analysis for binary traits. FINDINGS: Here we present a novel software package GENIE, which utilizes the power of multiple GPU or CPU processor cores to parallelize the interaction analysis. GENIE reads an entire genetic association study dataset into memory and partitions the dataset into fragments with non-overlapping sets of SNPs. For each fragment, GENIE analyzes: 1) the interaction of SNPs within it in parallel, and 2) the interaction between the SNPs of the current fragment and other fragments in parallel. We tested GENIE on a large-scale candidate gene study on high-density lipoprotein cholesterol. Using an NVIDIA Tesla C1060 graphics card, the GPU mode of GENIE achieves a speedup of 27 times over its single-core CPU mode run. CONCLUSIONS: GENIE is open-source, economical, user-friendly, and scalable. Since the computing power and memory capacity of graphics cards are increasing rapidly while their cost is going down, we anticipate that GENIE will achieve greater speedups with faster GPU cards. Documentation, source code, and precompiled binaries can be downloaded from http://www.cceb.upenn.edu/~mli/software/GENIE/.

| S-EPMC3115877 | biostudies-literature

Accelerating Spatial Cross-Matching on CPU-GPU Hybrid Platform With CUDA and OpenACC.

Project description:Spatial cross-matching operation over geospatial polygonal datasets is a highly compute-intensive yet an essential task to a wide array of real-world applications. At the same time, modern computing systems are typically equipped with multiple processing units capable of task parallelization and optimization at various levels. This mandates for the exploration of novel strategies in the geospatial domain focusing on efficient utilization of computing resources, such as CPUs and GPUs. In this paper, we present a CPU-GPU hybrid platform to accelerate the cross-matching operation of geospatial datasets. We propose a pipeline of geospatial subtasks that are dynamically scheduled to be executed on either CPU or GPU. To accommodate geospatial datasets processing on GPU using pixelization approach, we convert the floating point-valued vertices into integer-valued vertices with an adaptive scaling factor as a function of the area of minimum bounding box. We present a comparative analysis of GPU enabled cross-matching algorithm implementation in CUDA and OpenACC accelerated C++. We test our implementations over Natural Earth Data and our results indicate that although CUDA based implementations provide better performance, OpenACC accelerated implementations are more portable and extendable while still providing considerable performance gain as compared to CPU. We also investigate the effects of input data size on the IO / computation ratio and note that a larger dataset compensates for IO overheads associated with GPU computations. Finally we demonstrate that an efficient cross-matching comparison can be achieved with a cost-effective GPU.

| S-EPMC7497850 | biostudies-literature

Revisiting Molecular Dynamics on a CPU/GPU system: Water Kernel and SHAKE Parallelization.

Project description:We report Graphics Processing Unit (GPU) and Open-MP parallel implementations of water-specific force calculations and of bond constraints for use in Molecular Dynamics simulations. We focus on a typical laboratory computing-environment in which a CPU with a few cores is attached to a GPU. We discuss in detail the design of the code and we illustrate performance comparable to highly optimized codes such as GROMACS. Beside speed our code shows excellent energy conservation. Utilization of water-specific lists allows the efficient calculations of non-bonded interactions that include water molecules and results in a speed-up factor of more than 40 on the GPU compared to code optimized on a single CPU core for systems larger than 20,000 atoms. This is up four-fold from a factor of 10 reported in our initial GPU implementation that did not include a water-specific code. Another optimization is the implementation of constrained dynamics entirely on the GPU. The routine, which enforces constraints of all bonds, runs in parallel on multiple Open-MP cores or entirely on the GPU. It is based on Conjugate Gradient solution of the Lagrange multipliers (CG SHAKE). The GPU implementation is partially in double precision and requires no communication with the CPU during the execution of the SHAKE algorithm. The (parallel) implementation of SHAKE allows an increase of the time step to 2.0fs while maintaining excellent energy conservation. Interestingly, CG SHAKE is faster than the usual bond relaxation algorithm even on a single core if high accuracy is expected. The significant speedup of the optimized components transfers the computational bottleneck of the MD calculation to the reciprocal part of Particle Mesh Ewald (PME).

| S-EPMC3524996 | biostudies-literature

GPU/CPU Algorithm for Generalized Born/Solvent-Accessible Surface Area Implicit Solvent Calculations.

Project description:Molecular dynamics methodologies comprise a vital research tool for structural biology. Molecular dynamics has benefited from technological advances in computing, such as multi-core CPUs and graphics processing units (GPUs), but harnessing the full power of hybrid GPU/CPU computers remains difficult. The generalized Born/solvent-accessible surface area implicit solvent model (GB/SA) stands to benefit from hybrid GPU/CPU computers, employing the GPU for the GB calculation and the CPU for the SA calculation. Here, we explore the computational challenges facing GB/SA calculations on hybrid GPU/CPU computers and demonstrate how NAMD, a parallel molecular dynamics program, is able to efficiently utilize GPUs and CPUs simultaneously for fast GB/SA simulations. The hybrid computation principles demonstrated here are generally applicable to parallel applications employing hybrid GPU/CPU calculations.

| S-EPMC3464051 | biostudies-literature

Digital blood in massively parallel CPU/GPU systems for the study of platelet transport

Project description:We propose a highly versatile computational framework for the simulation of cellular blood flow focusing on extreme performance without compromising accuracy or complexity. The tool couples the lattice Boltzmann solver Palabos for the simulation of blood plasma, a novel finite-element method (FEM) solver for the resolution of deformable blood cells, and an immersed boundary method for the coupling of the two phases. The design of the tool supports hybrid CPU–GPU executions (fluid, fluid–solid interaction on CPUs, deformable bodies on GPUs), and is non-intrusive, as each of the three components can be replaced in a modular way. The FEM-based kernel for solid dynamics outperforms other FEM solvers and its performance is comparable to state-of-the-art mass–spring systems. We perform an exhaustive performance analysis on Piz Daint at the Swiss National Supercomputing Centre and provide case studies focused on platelet transport, implicitly validating the accuracy of our tool. The tests show that this versatile framework combines unprecedented accuracy with massive performance, rendering it suitable for upcoming exascale architectures.

| S-EPMC7739916 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data