SPEED UP SIMULATION

• Finding optimal resources

  A poor choice of job submission or simulation parameters leads to poor performance and a waste of computing resources. However, finding optimal MD engines and submission parameters in a complex HPC environment with heterogeneous hardware is a daunting and time-consuming challenge. We developed this web portal to simplify this task.

• How fast will a simulation run?

  A quick look at the chart of the maximum simulation speed of all MD executables tested on CC systems offers a quick idea of how long a simulation will take.

• Finding balance between speed and efficiency

  The fastest simulations may be inefficient due to poor parallel scaling to many CPUs or GPUs. Inefficient simulations consume more resources and impact priority, resulting in longer times in queue and less work done. Color-coded by efficiency chart allows choosing an optimal combination of speed and efficiency.

• Exploring the database

  All benchmarks are available from the Explore menu . This menu offers tools for querying, filtering, and viewing benchmark data.

• Viewing QM/MM benchmarks

  To view QM/MM benchmarks select simulation system 4cg1 in the Explore window .

Best Performance Of Each MD Engine Among All Clusters

Cost Of Simulations Using Best Performing CPU-only MD Engines

OPTIMIZING CPU USAGE

• Cost of a simulation

  Choosing fast and efficient job submission parameters is a good approach, but the total amount of computing resources used for a job, as we call it, cost, is more important. Three components define cost: CPU time, GPU time, and RAM. We measure cost in core years and GPU years. Core year is the equivalent of using one CPU core continuously for a full year and GPU year is the equivalent of using one GPU continuously for a full year.

• Minimizing queue time

  Scheduler controls resource usage to ensure that each user gets equal resources. If you use more resources than an average user, the scheduler will put your jobs on hold so that other users can catch up. You can avoid delays by minimizing the cost. Often you can significantly reduce cost by choosing simulations that are only slightly slower than the fastest but expensive ones .