cement-psaap/mcdc-backintrack

MC/DC-Back in Track: a lite version of MC/DC for testing and smooth integration of abstraction techniques

MC/DC-Back In Track

Immitating key features of MC/DC, this code repository is created to test implementations of the selected metaprogramming libraries (Numba,) that are investigated in MC/DC-TNT. This strategically helps achieving smooth integration of proposed abstraction ideas into MC/DC.

A particular goal of this repo is to demonstrate a working Python-based implementation, that supports abstractions in running mode (pure Python or Numba), MC algorithm (history-based or event-based), and kernel threading target (CPUs or GPUs). This is achieved by innovative uses of Python decorator and meta-classes, which adapt pure Python, scalar, history-based kernels into the desired running mode, MC algorithm, and threading target.

Achievements so far:

• Pure Python (history-based and event-based; only on CPU; useful for algorithm debugging)
• Numba history-based and event-based on CPU (serial)
• Numba event-based on GPU (unperformant)

TODO list:

1. GPU reduction on global/small tally (in this test code, neutron leakage). This may require designing a new adapter type.
2. Mesh tally. To implement the use of GPU atomics.
3. GPU exclusive scan for thread syncing and reproducibility. This completes branching-event adapter and allows running Numba event-based on GPU (but only with branchless collision).
4. GPU sorting for efficient particle bank memory access.
5. GPU adapter for multiplying events (such as fission and weight window). This allows running Numba event-based on GPU without branchless collision.
6. Consolidate different types of adapter.
7. Others: Run in multiple GPUs and nodes via MPI4Py. Introduce PyOMP for CPU threading. Implement particle consolidation in history-based for GPU run. ...