Kale, Laximant V. | Search NASA

DOE OSTI2012

Optimizing fine-grained communication in a biomolecular simulation application on Cray XK6

Achieving good scaling for fine-grained communication intensive applications on modern supercomputers remains challenging. In our previous work, we have shown that such an application -- NAMD -- scales well on the full Jaguar XT5 without long-range interactions; Yet, with them, the speedup falters beyond 64K cores. Although the new Gemini interconnect on Cray XK6 has improved network performance, the challenges remain, and are likely to remain for other such networks as well. We analyze communication bottlenecks in NAMD and its CHARM++ runtime, using the Projections performance analysis tool. Based on the analysis, we optimize the runtime, built on the uGNI library for Gemini. We present several techniques to improve the fine-grained communication. Consequently, the performance of running 92224-atom Apoa1 with GPUs on TitanDev is improved by 36%. For 100-million-atom STMV, we improve upon the prior Jaguar XT5 result of 26 ms/step to 13 ms/step using 298,992 cores on Jaguar XK6.

Sun, Yanhua↗

DOE OSTI2012

A uGNI-based Asynchronous Message-driven Runtime System for Cray Supercomputers with Gemini Interconnect

Gemini, the network for the new Cray XE/XK systems, features low latency, high bandwidth and strong scalability. Its hardware support for remote direct memory access enables efficient implementation of the global address space programming languages. Although the user Generic Network Interface (uGNI) provides a low-level interface for Gemini with support to the message-passing programming model (MPI), it remains challenging to port alternative programming models with scalable performance. CHARM++ is an object-oriented message-driven programming model. Its applications have been shown to scale up to the full Jaguar Cray XT machine. In this paper, we present an implementation of this programming model on uGNI for the Cray XE/XK systems. Several techniques are presented to exploit the uGNI capabilites by reducing memory copy and registration overhead, taking advantage of the persistent communication, and improving intra-node communication. Our microbenchmark results demonstrate that the uGNI-based runtime system outperforms the MPI-based implementation by up to 50% in terms of message latency. For communication intensive applications such as N-Queens, this implementation scales up to 15, 360 cores of a Cray XE6 machine and is 70% faster than the MPI-based implementation. In molecular dynamics application NAMD, the performance is also considerably improved by as much as 18%.

Computer Science↗