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calculating larger systems
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QC3 - HIGH PRECISION QUANTUM CHEMISTRY SIMULATION
Chemistry simulation is a vital part in a lot of fields including drug discovery, material science, etc. Quantum chemistry based simulations gain more field with their higher precision but lack the required throughput due to the steep computational costs. The QC3 module can help bridge this problem by having superior speed compared to other kinds of solutions.
THE BENEFITS OF BOTH WORLDS
Psi4 is a feature rich quantum chemistry software having solutions for Density Functional Theory (DFT), Moller-Plesset perturbation Theory (MP2), and Coupled-Cluster (CC) type of calculations. Its C++/Python core with clean interfaces and modular build makes it a prime candidate to cooperate with the QC3 module. It has a wide user and developer base ranging from drug research to theoretical chemists, from quantum computing to machine learning experts.
Our software solution (QC3) for simulating systems with thousands of atoms is a magnitude faster than one of the market leading solution. This is caused by our unique technology of using GPUs as calculation backend. As far as we concerned this is the first fully functional ab-initio simulator that uses GPU as the main computation device for high accuracy quantum chemical calculations. QC3 enables a more accurate and time-efficient simulation of large molecules of high angular momentum orbitals and quantum systems.
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Powerful & Intuitive Interface
Our award winning user interface gives youfull control and a complete overview.
Chemistry simulation is a vital part in a lot of fields including drug discovery, material science, etc. Quantum chemistry based simulations gain more field with their higher precision but lack the required throughput due to the steep computational costs. The QC3 module can help bridge this problem by having superior speed compared to other kinds of solutions.
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PUBLICATIONS
Calculation of quantum chemical two-electron integrals by applying compiler technology on GPU
In this article, we present an effective approach to calculate quantum chemical two-electron integrals over basis sets consisting of Gaussian-type basis functions on graphical processing unit (GPU). Our framework generates several different variants called routes to the same integral problem with different ways to the solution. Each route is benchmarked and the best is selected for each GPU architecture.
Towards chemically accurate QM/MM simulations on GPUs
In the recent years, implementing MD simulations on graphics processors has gained a large interest, with multiple popular software packages including some form of GPU‐acceleration support. Different approaches have been developed regarding various aspects of the algorithms, with important differences in the specific solutions. Focusing on published works in the field of classical MD, we describe the chosen implementation methods and algorithmic techniques used for porting to GPU, as well as how recent advances of GPU architectures will provide even more optimization possibilities in the future.
Classical molecular dynamics on graphics processing unit architectures
In the recent years, implementing MD simulations on graphics processors has gained a large interest, with multiple popular software packages including some form of GPU‐acceleration support. Different approaches have been developed regarding various aspects of the algorithms, with important differences in the specific solutions. Focusing on published works in the field of classical MD, we describe the chosen implementation methods and algorithmic techniques used for porting to GPU, as well as how recent advances of GPU architectures will provide even more optimization possibilities in the future.
Optimized GPU implementation of Merck molecular force field and universal force field
In this article we demonstrate novel GPU implementations of the well known Merck Molecular Force Field (MMFF94) and Universal Force Field (UFF) algorithms which are near to utilize the theoretical peak performance of the GPU the software runs on. A double-precision speedup of 55× for MMFF94 and 140× for UFF is achieved, with the factor being 90× for the single-precision implementation of MMFF94.
The BRUSH algorithm for two-electron integrals on GPU
This Letter presents a new algorithmic method developed to evaluate two-electron repulsion integrals based on contracted Gaussian basis functions in a parallel way. This new algorithm scheme provides distinct SIMD (single instruction multiple data) optimized paths which symbolically transforms integral parameters into target integral algorithms. Our measurements indicate that the method gives a significant improvement over the CPU-friendly PRISM algorithm. The benchmark tests (evaluation of more than 108 integrals using the STO-3G basis set) of our GPU (NVIDIA GTX 780) implementation showed up to 750-fold speedup compared to a single core of Athlon II X4 635 CPU.
HIGH PROFILE ACADEMIC TEAM WITH INTERNATIONALLY RECOGNIZED PUBLICATIONS, & ACTIVE UNIVERSITY POSITIONS
Tornai Gábor PhD.
(Team Leader)
He received his MSc degree in computer engineering with honors in 2009 from the Pázmány Péter Catholic University, Faculty of Information Technology and Bionics. He received his PhD degree in 2014. The title of his dissertation was Medical image processing on kilo processor architectures. He won second prize at national scientific student conference both as a student and scientific advisor. Based on his academic work he won the Hungarian Government Scholarship twice. His research interest is mainly focused on GPU optimization, medical imaging algorithms, and numerical methods. He submitted several conference and research papers connected to these research areas.
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Kis Gergely
(Lead Developer)
He received his MSc degree in computer engineering with honors in 2012 from the Budapest University of Technology and Economics. Earlier he received second prize at the International Mathematical Olympiad (IMO), and the second prize at the International University Mathematics Competition (IMC), and he received first prize five times in the mathematics competition of the university. His university research was related to the GPGPU-based visualization of 3D medical data. He had worked on holographic microscope images processing at MTA SZTAKI for two years. He took part in the development of camera tracking and 3D spatial scheme and since 2013 he has been participating in the development of a GPGPU-based quantum chemical simulation software.
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Ádám Rák PhD.
(GPU Expert)
Graduating as a computer engineer with a PhD in information science, his primary interests include massively parallel computer architectures, compiler technology, and machine learning. A patent owner and author, he obtained his MSc with honors in computer engineering in 2009 and completed his PhD in 2014, focusing on the Stream-based RACER array processor and algorithm implementation methods. Recognized at national scientific student conferences, he automated the optimization of nonlinear, chaotic, analog circuits. A two-time recipient of the Hungarian Government Scholarship, his research emphasizes GPU optimization, quantum chemistry simulation algorithms, and computer-aided design. He holds numerous patents in these areas.
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György Cseray PhD
(Advisor)
An electrical engineer and Ph.D. holder in infobionics, he serves as Innovation Vice-Dean at Pázmány Péter Catholic University (PPCU) and Vice-Dean at the Faculty of Information Technology and Bionics. With an M.Sc. in electrical engineering from the University of Technology and Economics, Budapest, he contributed to a Notre Dame University project on Cellular Nonlinear Networks (CNN) during his Ph.D. studies. Awarded his Ph.D. in Infobionics by PPCU in 2006, he specializes in multiprocessor systems, cellular neural networks, GPU and CNN computational aspects, and artificial immune system applications. His supervision includes projects on GPU-accelerated CNN simulators, GPU-powered artificial immune systems, and student-led developments in GPU-based cloud computing platforms. As an IEEE member, he demonstrates a steadfast commitment to his field.
A pioneering step with QC3 Cloud Computing
Read how VERDI Solution experienced working with QC3 Compute Cloud.
Dr. Julianna Lisziewicz
We at VERDI Solutions are excited to share our experience as the first testers of the groundbreaking QC3 cloud computing software. Having been offered the unique opportunity to explore its capabilities, we are thrilled to report that the results were more than impressive. This testimonial stands as a testament to the groundbreaking impact QC3 has had on our operations.
Compared to the other widely used cloud providers, the QC3 cloud solution not only exceeded our but also exceeded expectations. It delivers an exceptional 2-3 times faster computing speed at the same cost. This significant advance is not only a technological advancement, but also a strategic advantage that has changed the the landscape of our research.
The accelerated calculations provided by QC3 have not only resulted in a considerable cost advantage also drastically reduced the time needed for crucial calculations in our vaccine development processes. This increased efficiency leads to a remarkable increase in our time-to-market and positions us at the forefront of vaccine development in the biotech industry.