A discussion of many of the recently implemented features of GAMESS (General Atomic and Molecular Electronic Structure System) and LibCChem (the C++ CPU/GPU library associated with GAMESS) is presented. These features include fragmentation methods such as the fragment molecular orbital, effective fragment potential and effective fragment molecular orbital methods, hybrid MPI/OpenMP approaches to Hartree-Fock, and resolution of the identity second order perturbation theory. Many new coupled cluster theory methods have been implemented in GAMESS, as have multiple levels of density functional/tight binding theory. The role of accelerators, especially graphical processing units, is discussed in the context of the new features of LibCChem, as it is the associated problem of power consumption as the power of computers increases dramatically. The process by which a complex program suite such as GAMESS is maintained and developed is considered. Future developments are briefly summarized.

/pdf/recent-developments-in-the-general-atomic-and-molecular-2fufj6tbv5.pdf

Recent developments in the general atomic and molecular electronic structure system.

Graphics processing units (GPUs) have traditionally been used in molecular modeling solely for visualization of molecular structures and animation of trajectories resulting from molecular dynamics simulations. Modern GPUs have evolved into fully programmable, massively parallel co-processors that can now be exploited to accelerate many scientific computations, typically providing about one order of magnitude speedup over CPU code and in special cases providing speedups of two orders of magnitude. This paper surveys the development of molecular modeling algorithms that leverage GPU computing, the advances already made and remaining issues to be resolved, and the continuing evolution of GPU technology that promises to become even more useful to molecular modeling. Hardware acceleration with commodity GPUs is expected to benefit the overall computational biology community by bringing teraflops performance to desktop workstations and in some cases potentially changing what were formerly batch-mode computational jobs into interactive tasks.

GPU-accelerated molecular modeling coming of age

http://www.ks.uiuc.edu/Research/gpu/files/rdfgpu/rdfgpu.pdf

Fast analysis of molecular dynamics trajectories with graphics processing units-Radial distribution function histogramming

An efficient integral library Libcint was designed to automatically implement general integrals for Gaussian-type scalar and spinor basis functions. The library is able to evaluate arbitrary integral expressions on top of p, r and σ operators with one-electron overlap and nuclear attraction, two-electron Coulomb and Gaunt operators for segmented contracted and/or generated contracted basis in Cartesian, spherical or spinor form. Using a symbolic algebra tool, new integrals are derived and translated to C code programmatically. The generated integrals can be used in various types of molecular properties. To demonstrate the capability of the integral library, we computed the analytical gradients and NMR shielding constants at both nonrelativistic and 4-component relativistic Hartree–Fock level in this work. Due to the use of kinetically balanced basis and gauge including atomic orbitals, the relativistic analytical gradients and shielding constants requires the integral library to handle the fifth-order electron repulsion integral derivatives. The generality of the integral library is achieved without losing efficiency. On the modern multi-CPU platform, Libcint can easily reach the overall throughput being many times of the I/O bandwidth. On a 20-core node, we are able to achieve an average output 8.3 GB/s for C60 molecule with cc-pVTZ basis. © 2015 Wiley Periodicals, Inc.

Libcint: An efficient general integral library for Gaussian basis functions.

TeraChem was born in 2008 with the goal of providing fast on‐the‐fly electronic structure calculations to facilitate ab initio molecular dynamics studies of large biochemical systems such as photoswitchable proteins and multichromophoric antenna complexes. Originally developed for videogaming applications, graphics processing units (GPUs) offered a low‐cost parallel computer architecture that became more accessible for general‐purpose GPU computing with the release of CUDA in 2007. The evaluation of the electron repulsion integrals (ERIs) is a major bottleneck in electronic structure codes and provides an attractive target for acceleration on GPUs. Thus, highly efficient routines for evaluation of and contractions between the ERIs and density matrices were implemented in TeraChem. Electronic structure methods were developed and implemented to leverage these integral contraction routines, resulting in the first quantum chemistry package designed from the ground up for GPUs. This GPU acceleration makes TeraChem capable of performing large‐scale ground and excited state calculations in the gas and condensed phase. Today, TeraChem's speed forms the basis for a suite of quantum chemistry applications, including optimization and dynamics of proteins, automated and interactive chemical discovery tools, and large‐scale nonadiabatic dynamics simulations.

TeraChem: A graphical processing unit-accelerated electronic structure package for large-scale ab initio molecular dynamics

An implementation is presented of an uncontracted Rys quadrature algorithm for electron repulsion integrals, including up to g functions on graphical processing units (GPUs). The general GPU programming model, the challenges associated with implementing the Rys quadrature on these highly parallel emerging architectures, and a new approach to implementing the quadrature are outlined. The performance of the implementation is evaluated for single and double precision on two different types of GPU devices. The performance obtained is on par with the matrix-vector routine from the CUDA basic linear algebra subroutines (CUBLAS) library.

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1538&context=chem_pubs

Uncontracted Rys Quadrature Implementation of up to G Functions on Graphical Processing Units.

Ultrasound tomography is an emerging modality for breast imaging. However, most current ultrasonic tomography imaging algorithms, historically hindered by the limited memory and processor speed of computers, are based on ray theory and assume a homogeneous background which is inaccurate for complex heterogeneous regions. Therefore, wave theory, which accounts for diffraction effects, must be used in ultrasonic imaging algorithms to properly handle the heterogeneous nature of breast tissue in order to accurately image small lesions. However, application of waveform tomography to medical imaging has been limited by extreme computational cost and convergence. By taking advantage of the computational architecture of Graphic Processing Units (GPUs), the intensive processing burden of waveform tomography can be greatly alleviated. In this study, using breast imaging methods, we implement a frequency domain waveform tomography algorithm on GPUs with the goal of producing high-accuracy and high-resolution breast images on clinically relevant time scales. We present some simulation results and assess the resolution and accuracy of our waveform tomography algorithms based on the simulation data.

Toward a practical ultrasound waveform tomography algorithm for improving breast imaging

The 2010 MEMOCODE Hardware Software Co-design challenge is to implement a Deep Packet Inspection architecture, called the CANSCID - Combined Architecture for Stream Categorization and Intrusion Detection. In this short paper, we present the design details of our submission, that utilizes a Graphical Processing Unit (GPU) to accelerate the parallel regular expression matching. The target line rate of 500 Mbps is met on all of the 25 mandatory and 10 optional patterns. The design is developed using the NVIDIA CUDA framework and tested on the Tesla GPU.

http://ieeexplore.ieee.org/iel5/5550962/5558619/05558644.pdf

Canscid-cuda

Commodity clusters augmented with application accelerators are evolving as competitive high performance computing systems. The Graphical Processing Unit (GPU) with a very high arithmetic density and performance per price ratio is a good platform for the scientific application acceleration. In addition to the interconnect bottlenecks among the cluster compute nodes, the cost of memory copies between the host and the GPU device have to be carefully amortized to improve the overall efficiency of the application. Scientific applications also rely on efficient implementation of the Basic Linear Algebra Subroutines (BLAS), among which the General Matrix Multiply (GEMM) is considered as the workhorse subroutine. In this paper, we study the performance of the memory copies and GEMM subroutines that are crucial to port the computational chemistry algorithms to the GPU clusters. To that end, a benchmark based on the NetPIPE [1] framework is developed to evaluate the latency and bandwidth of the memory copies between the host and the GPU device. The performance of the single and double precision GEMM subroutines from the NVIDIA CUBLAS 2.0 library are studied. The results have been compared with that of the BLAS routines from the Intel Math Kernel Library (MKL) to understand the computational trade-offs. The test bed is a Intel Xeon cluster equipped with NVIDIA Tesla GPUs.

https://digital.library.unt.edu/ark:/67531/metadc932634/m2/1/high_res_d/965387.pdf

Performance analysis of memory transfers and GEMM subroutines on NVIDIA Tesla GPU cluster

Ultrasound tomography (UST) is a breast imaging modality that is radiation free, operator independent, and does not involve breast compression. In the UST system under consideration, the breast is surrounded by a transducer ring that moves along the coronal axis from the chest wall to the nipple region. The deployment of UST in a clinical setting is technically challenging from three major standpoints: acquisition speed, storage capability, and computational requirements. Data acquisition must be fast to maximize patient throughput and minimize image registration artifacts. Unlike traditional ultrasound, hundreds of gigabytes of data must be acquired, buffered, and processed to image various characteristics of breast tissues such as sound speed, attenuation, and reflectivity. The tomographic image reconstruction methods are non-linear, iterative algorithms with significant computational complexity. Moreover, the scanner hosting the acquisition and reconstruction components must satisfy stringent cost, power, and size requirements. For decades, the above constraints have hindered the practicality of UST in a clinical scenario. We describe the design of a UST system that addresses the relevant clinical requirements as a means to demonstrate the feasability of UST deployment in a clinical setting.

Breast imaging using ultrasound tomography: From clinical requirements to system design

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