Sarkas is a pure-python molecular dynamics (MD) code we are currently developing for wide (open source) use. To our knowledge, Sarkas is the first production-scale pure-Python molecular dynamics code designed for simulating plasmas. Current areas of application are strongly coupled plasmas which include dusty plasmas, ultracold neutral plasmas, and warm dense matter.
Sarkas offers the ease of use of Python while being highly performant with execution speeds comparable to or exceeding that of compiled languages (e.g., C). Sarkas’s high-performance originates from extensive use of Numpy arrays and Numba’s just-in-time compilation.
The documentation with tutorials on the current capabilities of Sarkas can be found here.
Here, we show how MD can be used to study kinetic instabilities in plasmas. In the movie below, the vertical axis is particle velocity and the horizontal axis is space (that is, it is phase space). The plasma is mostly composed of particles in equilibrium (red points) with a bump on the tail (blue particles at large, positive velocity). This situation mimics a plasma with an injected pulse of fast particles in it. What is interesting about this MD simulation is that it resolves particles down to their atomic scale, while successfully generating longer wavelength instabilities, thereby spanning extreme length scales. This shows promise for using Sarkas to study a wider range of kinetic and hydrodynamic phenomena building from the atomistic level.
Interparticle interactions currently included are long-range Coulomb and intermediate- to short-range screened Coulomb (Yukawa interactions). The computational cell is 3D periodic cubic. For short range potentials, O(N) scaling is achieved with a neighbor list (linked cell); conversely, for intermediate- to long-range forces the Particle-Particle Particle-Mesh (PPPM) algorithm is employed. Details of the theoretical and numerical aspects can be found in the paper linked below.
As an example, MD simulations of strongly coupled plasmas enable access to the plasma’s static and dynamic properties. Static structural information is obtained through the radial distribution function g(r) using the particle positions. Dynamical properties such as transport processes are also available by computing the transport coefficients from the particle trajectories. As a example, in the figure below the results of dynamic structure factor S(k,ω) for a Yukawa plasma, and the resulting wave dispersion properties.
To illustrate performance of Sarkas
we compared the execution times with a equivalent version of the code written entirely in C. The figure below compares C and Python
timings of two computationally intensive parts of the code: LCL for the Particle-Particle part and charge assignment to a grid for the Particle-Mesh part. The times(Intel Xeon E5-2670v2, 2.6GHz) are for one step of the force calculation for 104
particles. The performance of Sarkas
is similar to that of the C version, even with C’s O3 compiler optimization
. This is a remarkable result considering that Sarkas
is a pure Python (with Numba) code.
You can learn more about our recent work here:
In its current form Sarkas uses Ewald generalized to intermediate range interactions. Many extensions to the code are expected. Stay tuned or Join us!