![]() ![]() This blog post provides details on how different types of compute instance configurations perform on a given problem. ![]() This ensemble method relies mainly on throughput of how many simulations a high performance computing (HPC) system is able to complete in a given timeframe. To get results faster when trying to arrive at an average understanding of the system across multiple parameters, we run hundreds of copies of the simulation in parallel. ![]() ![]() This method necessitates the use of special high-performance interconnects keeping the inter process communication overhead low to linearly scale out the simulation. You can reduce time to results by parallelizing the simulation across multiple compute instances. MD applications are typically tightly coupled workloads where the system of atoms are distributed into multiple domains to attain parallelism and there is significant information exchanged across domains. The simulations run for hours (sometimes days) in order to get to meaningful lengthier timescales, and gain insights on final confirmation of a molecule. We measure the performance of an MD simulation as nano-seconds per day (Ns/day). The typical time scales of the simulated system are in the order of micro-seconds (Ms) or nano-seconds (Ns). The importance of MD came to bear on recent efforts for the SARS-COV-2 vaccine where MD applications such as GROMACS helped researchers identify molecules that bind to the spike protein of the virus and block it from infecting human cells. MD simulations are used across various domains such as material sciences, biochemistry, biophysics and are typically used in two broad ways to study a system. Molecular dynamics (MD) is a simulation method for analyzing the movement and tracing trajectories of atoms and molecules where the dynamics of a system evolve over time. ![]()
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