ACEMD And OpenMM Unite To Tackle New Challenges In Molecular Simulations
First, A Bit Of History…
In 2008, ACEMD was the first molecular dynamics software using GPUs (graphical processing units) to accelerate bio-molecular simulations. ACEMD came from a GPU conversion of 2006 CellMD, the first MD code to run on commercially available consumer-grade accelerator processors. ACEMD allowed us to demonstrate unprecedented simulation speeds for a decade, which was unmatched by any CPU-only or GPU software. After more than a decade, we can say for certain that the decision to invest in GPU-based technology was a correct one. GPUs have become an integral part of scientific computing, new architectures are announced almost on an annual basis, and the performance just keeps growing.
Nevertheless, GPUs are not novel anymore, and enabling more science by just significantly improving the speed of MD engines is no longer a possibility. In the last years, our research and development team has focused on new challenges, with higher immediate impact such as machine learning, HTMD for handling high-throughput molecular simulations using adaptive sampling and Markov state models, and finally PlayMolecule, an infrastructure for molecular discovery.
Following the lead of ACEMD, many MD applications started to adapt to GPUs, and now it would be hard to find any popular MD software without GPU capabilities. Among them was OpenMM, an open-source project born in 2008 as a minimal but versatile C++ framework with a simple Python API enabling access to high performance GPU-accelerated MD capabilities, attracting developers and users from academia. Like ACEMD, OpenMM was designed and implemented with GPU computing in mind, and eventually exceeded our software in terms of functionality, and more recently, in performance.
In 2017, we made the decision to embrace OpenMM and started to develop the next generation ACEMD using several parts of OpenMM C++ backend, in the same way that we were already using CUDA libraries for FFT. In 2019, we released ACEMD v3, which merged the previous ACEMD codebase with OpenMM kernels. Among other things, this gave ACEMD multithreaded CPU support and potential access to more features. In the first months of 2020, we have already submitted a pull request to integrate ACEMD multi-time step integrators into OpenMM, a feature which currently gives a 10% improvement in speed to ACEMD. Other open source contributions will appear soon.
OpenMM and ACEMD remain different in several aspects. ACEMD is a simple, stand-alone MD executable, rather than a library. OpenMM provides a library of low-level molecular simulation capabilities (force field terms, integrators, thermostats, etc), while ACEMD relies on the Python framework HTMD and forcefield tools to build, manage and analyze simulations. ACEMD focuses on stability, reliability, performance, easy-to-use and professional support, in a similar way as RedHat provides a commercial package for Linux. ACEMD has also its own additional tests and integration with other tools developed by Acellera.
Our Joint Collaborative Project…
Today (27/5/2020), thanks to a one-year seed grant from the Chan Zuckerberg Initiative (CZI) Acellera is joining the OpenMM development team, together with lead OpenMM developer Peter Eastman, Tom Markland from Stanford University (whose lab focuses on QM/MM and machine learning for quantum chemistry), John Chodera from the Sloan Kettering Institute (whose lab focuses on free energy calculations). This grant aims to support the continued development of OpenMM to better serve its broad biomolecular modeling community, and its extension to integrate machine learning to enable genomic-scale biomolecular modeling, simulation, and prediction. Our collaborative project aims to secure long-term sustainable federal funding for OpenMM from the National Institutes of Health in a proposal submitted earlier this year.
“Hundreds of thousands of scientists each day use open source software to carry out their research,” said CZI Head of Science Cori Bargmann. “Scientists deserve better tools, and we’re helping to meet that need by supporting open source projects that will advance biomedical science and foster greater access to critical software.”
This new series of year-long grants of the CZI’s Essential Open Source Software for Science program, aims to support open source software projects essential to biomedical research, enabling software maintenance, growth, development, and community engagement. View the full list of grantees. Open source software is crucial to modern scientific research, advancing biology and medicine while providing reproducibility and transparency. Yet even the most widely used research software often lacks dedicated funding.
Prof. Gianni De Fabritiis, head of the Computational Science Laboratory (Universitat Pompeu Fabra) and CEO/CSO at Acellera thinks that “this is the way forward. By joining forces we can have one of the largest development teams in molecular simulations and have the strength to tackle the most challenging research projects ahead to make ACEMD and OpenMM incredibly useful for the research community.”