Seriema: RDMA-based Remote Invocation with a Case-Study on Monte-Carlo Tree Search
Hammurabi Mendes, Bryce Wiedenbeck, Aidan O'Neill · 2022
We introduce Seriema, a middleware that integrates RDMA-based remote invocation, asynchronous data transfer, NUMA-aware automatic management of registered memory, and message aggregation in idiomatic C++1x, targeted for distributed data structure support for ML applications that benefit both from low-latency communication and message aggregation for high throughput. We evaluate the usability of Seriema by implementing a Monte-Carlo Tree Search (MCTS) application framework, which runs distributed simulations given only a sequential problem specification. Micro-benchmarks show that Seriema provides remote invocations with low overhead, and that our MCTS application framework scales well up to the number of non-hyperthreaded CPU cores while simulating plays of the board game Hex.