Yuri Alexeev

GAMESS As a Free Quantum-Mechanical Platform for Drug Research

Driven by a steady improvement of computational hardware and significant progress in ab initio method development, quantum-mechanical approaches can now be applied to large biochemical systems and drug design. We review the methods implemented in GAMESS, which are suitable to calculate large biochemical systems. An emphasis is put on the fragment molecular orbital method (FMO) and quantum mechanics interfaced with molecular mechanics (QM/MM). The use of FMO in the protein-ligand binding, structure-activity relationship (SAR) studies, fragment- and structure-based drug design (FBDD/SBDD) is discussed in detail.

Heuristic static load-balancing algorithm applied to the fragment molecular orbital method

In the era of petascale supercomputing, the importance of load balancing is crucial. Although dynamic load balancing is widespread, it is increasingly difficult to implement effectively with thousands of processors or more, prompting a second look at static load-balancing techniques even though the optimal allocation of tasks to processors is an NP-hard problem. We propose a heuristic static load-balancing algorithm, employing fitted benchmarking data, as an alternative to dynamic load balancing. The problem of allocating CPU cores to tasks is formulated as a mixed-integer nonlinear optimization problem, which is solved by using an optimization solver. On 163,840 cores of Blue Gene/P, we achieved a parallel efficiency of 80% for an execution of the fragment molecular orbital method applied to model protein-ligand complexes quantum-mechanically. The obtained allocation is shown to outperform dynamic load balancing by at least a factor of 2, thus motivating the use of this approach on other coarse-grained applications.

Ligand binding to an Allergenic Lipid Transfer Protein Enhances Conformational Flexibility resulting in an Increase in Susceptibility to Gastroduodenal Proteolysis

Non-specific lipid transfer proteins (LTPs) are a family of lipid-binding molecules that are widely distributed across flowering plant species, many of which have been identified as allergens. They are highly resistant to simulated gastroduodenal proteolysis, a property that may play a role in determining their allergenicity and it has been suggested that lipid binding may further increase stability to proteolysis. It is demonstrated that LTPs from wheat and peach bind a range of lipids in a variety of conditions, including those found in the gastroduodenal tract. Both LTPs are initially cleaved during gastroduodenal proteolysis at three major sites between residues 39–40, 56–57 and 79–80, with wheat LTP being more resistant to cleavage than its peach ortholog. The susceptibility of wheat LTP to proteolyic cleavage increases significantly upon lipid binding. This enhanced digestibility is likely to be due to the displacement of Tyr79 and surrounding residues from the internal hydrophobic cavity upon ligand binding to the solvent exposed exterior of the LTP, facilitating proteolysis. Such knowledge contributes to our understanding as to how resistance to digestion can be used in allergenicity risk assessment of novel food proteins, including GMOs.

Machine-Learning-Based Load Balancing for Community Ice Code Component in CESM

Load balancing scientific codes on massively parallel architectures is becoming an increasingly challenging task. In this paper, we focus on the Community Earth System Model, a widely used climate modeling code. It comprises six components each of which exhibits different scalability patterns. Previously, an analytical performance model has been used to find optimal load-balancing parameter configurations for each component. Nevertheless, for the Community Ice Code component, the analytical performance model is too restrictive to capture its scalability patterns. We therefore developed machine-learning-based load-balancing algorithm. It involves fitting a surrogate model to a small number of load-balancing configurations and their corresponding runtimes. This model is then used to find high-quality parameter configurations. Compared with the current practice of expert-knowledge-based enumeration over feasible configurations, the machine-learning-based load-balancing algorithm requires six times fewer evaluations to find the optimal configuration.

The Heuristic Static Load-Balancing Algorithm Applied to the Community Earth System Model

We propose to use the heuristic static load-balancing (HSLB) algorithm for solving load-balancing problems in the Community Earth System Model (CESM), a climate model, using fitted benchmark data as an alternative to the current manual approach. The problem of allocating the optimal number of CPU cores to CESM components is formulated as a mixed-integer nonlinear optimization problem which is solved by using an optimization branch-and-bound solver implemented in the MINLP package MINOTAUR. The key feature of the branch-and-bound method is that it guarantees to provide an optimal solution or show that none exists. Our algorithm was tested for the 1° and 1/8° resolution simulations on 32,768 nodes (131,072 cores) of IBM Blue Gene/P where we consistently achieved well load-balanced results. This work is a part of a broader effort to eliminate the need for manual tuning of the code for each platform and simulation type, improve the performance and scalability of CESM, and develop automated tools to achieve these goals.

An efficient MPI/openMP parallelization of the Hartree-Fock method for the second generation of Intel ® Xeon Phi ™ processor

Modern OpenMP threading techniques are used to convert the MPI-only Hartree-Fock code in the GAMESS program to a hybrid MPI/OpenMP algorithm. Two separate implementations that differ by the sharing or replication of key data structures among threads are considered, density and Fock matrices. All implementations are benchmarked on a super-computer of 3,000 Intel® Xeon Phi™ processors. With 64 cores per processor, scaling numbers are reported on up to 192,000 cores. The hybrid MPI/OpenMP implementation reduces the memory footprint by approximately 200 times compared to the legacy code. The MPI/OpenMP code was shown to run up to six times faster than the original for a range of molecular system sizes.

An efficient MPI/OpenMP parallelization of the Hartree–Fock–Roothaan method for the first generation of Intel® Xeon Phi™ processor architecture

The Hartree–Fock method in the General Atomic and Molecular Structure System (GAMESS) quantum chemistry package represents one of the most irregular algorithms in computation today. Major steps in the calculation are the irregular computation of electron repulsion integrals and the building of the Fock matrix. These are the central components of the main self consistent field (SCF) loop, the key hot spot in electronic structure codes. By threading the Message Passing Interface (MPI) ranks in the official release of the GAMESS code, we not only speed up the main SCF loop (4× to 6× for large systems) but also achieve a significant ( > 2 ×) reduction in the overall memory footprint. These improvements are a direct consequence of memory access optimizations within the MPI ranks. We benchmark our implementation against the official release of the GAMESS code on the Intel® Xeon Phi™ supercomputer. Scaling numbers are reported on up to 7680 cores on Intel Xeon Phi coprocessors.

A systematic study of minima in alanine dipeptide: A Systematic Study of Minima in Alanine Dipeptide

The alanine dipeptide is a standard system to model dihedral angles in proteins. It is shown that obtaining the Ramachandran plot accurately is a hard problem because of many local minima; depending on the details of geometry optimizations, different Ramachandran plots can be obtained. To locate all energy minima, starting from geometries from MD simulations, 250,000 geometry optimizations were performed at the level of RHF/6‐31G*, followed by re‐optimizations of the located 827 minima at the level of MP2/6–311++G**, yielding 30 unique minima, most of which were not previously reported in literature. Both in vacuo and solvated structures are discussed. The minima are systematically categorized based on four backbone dihedral angles. The Gibbs energies are evaluated and the structural factors determining the relative stabilities of conformers are discussed.

Power Measurements of Hartree-Fock Algorithms Using Different Storage Devices

In this paper, we analyze energy consumption characteristics of calculation runs with different implementations of the fundamental quantum chemistry method called Hartree-Fock in the popular quantum chemistry package GAMESS. The Hartree-Fock algorithms vary in how electron repulsion integrals are recomputed or stored in memory or disk. Based on our results, storing electron repulsion integrals in memory is shown to be more energy efficient if the storage bandwidth is equal to or higher than the speed of integral generation. Our theoretical models predict that the optimal strategy for improving energy efficiency for Hartree-Fock is to re-compute integrals with high angular momentum and store other integrals in memory or with the recent generation of solid state drives (SSDs). According to our results, the overall energy saving from storing low angular momentum integral in memory rather than re-computing them is 6-10 times for modern Intel® processors. It is, to the best of our knowledge, the first study to improve energy efficiency of Hartree-Fock by examining different options for storing various types of electron repulsion integrals.