A CUDA-Based 3D Kinetic Model for Space Plasma Physics Shahab - PowerPoint PPT Presentation

A CUDA-Based 3D Kinetic Model for Space Plasma Physics Shahab Fatemi and Andrew R. Poppe Space Sciences Laboratory UC Berkeley, CA, USA GPU T echnology Conference April 7, 2016 Image courtesy of S. Saarloos

[NASA]  Plasma is the fourth state of matter.  Highly ionized gas, often >100,000 Kelvin.  Mainly consists of electrons and ions (charged particles).  Solar wind (supersonic fmow of plasma from the Sun) typical speed near Earth: ~400 km/s (~895,000 mi/h) 2

Solar wind plasma interacts with difgerent objects in difgerent ways. Mars Earth [Artistic work: NASA/GSFC] 3

A visual proof for solar wind interaction with the Earth: Northern lights (aurora) [ C a n a d a . P h o t o g r a p h e r : D a n i e l J . C o x / C o r b i s ] Aurora on Jupiter, but not visible by eye (Ultra Violet). [John Clarke (University of Michigan)] 4

Why are these important?  Solar wind interaction with the Earth impacts our daily life.  Astronauts, and spacecrafts in space, and their safety.  Fundamental information on the history of planetary evolution Mars has lost most of its atmosphere through interaction with solar wind plasma.  Space weather monitors solar activity.  Solar wind has been interacting with the Earth over billions of years, since the Earth was born. 5

Modeling/Simulations In-situ observations [STEL, Nagoya University] [NASA/New Horizons] Lab experiments 6 [Vacuum chamber/University of Colorado]

Modeling techniques Magnetohydrodynamic (MHD) Fluid For global scale simulations Hybrid (Kinetic-Fluid) Similar to PIC models For medium scale simulations Boltzmann-Vlasov Kinetic Particle-in-cell (PIC) For small scale simulations 7

Motivation Major problems in HPC in space and plasma physics:  Network latency (communication)  Processor load imbalance (source and loss processes)  High cost to maintain super computers  CPU performance HPC using CPUs in space and plasma modeling has reached a state that cannot satisfy all our needs. We need to use new technologies. We need GPUs! 8

Kinetic-particle method Similar to n-body problems. Move charged particles. Solve electromagnetic (wave) equations. 9

Our GPU-based kinetic model In our implementation, we use Single-CPU Single-GPU 10

Plasma motion Concept of plasma motion is difgerent than neutral atoms and thermal gases. 11

Particle mapping to grid 12

Performance (naïve implementation) Grid size: 100x100x100 Particles per cell: 64 (total: 64M particles) 13

Particle mapping to grid Particles are marked in every block of particles. A thread-block is assigned to a block of particles. Particles are sorted as they move between grid cells. 14

Comparison Grid size: 100x100x100 Particles per cell: 64 (total: 64M particles) Function Naïve (ms) Advance (ms) Speed up Particle motion 46.6 38.4 ~1.20 Particle mapping 261.7 189.6 ~1.38 Field mapping 187.3 84.9 ~2.20 Atomic operations are not all that bad! 15

Application 1 Plasma interaction with Ganymede (the largest moon in our solar system) Moon Ganymede Earth 16

Fair Comparison A fair comparison is always a challenge! We made two identical simulation runs (identical grid cells, particle number, time steps) Run #1) 288 Intel processors (6 nodes x 48 CPUs) Without GPUs Run #2) 1 Intel processor + single Titan X GPU. Speed up: Cost not included Model Run time (h) Cost ($) 158/124 =1.27 CPU-based ~158 ~(60K?) Cost included GPU-based ~124 ~1K 158*60/124 = 76 Be fair and keep developing! 17

Application 2 Real-time simulation of plasma interaction with Mars. Mars Video here! 18

Summary  The fjrst GPU based 3D kinetic model in space and plasma.  New algorithms introduced in our model.  We can now take a step forward to solve more complex problems. Future work:  Implement a multi-GPU model  Improvement in our algorithms 19

Field Solver We use fjnite difgerence approximation to solve our electromagnetic equations. 21