The documents distributed by this server have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a noncommercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.
Publications of SPCL
|T. Hoefler, T. Schneider and A. Lumsdaine:|
|Characterizing the Influence of System Noise on Large-Scale Applications by Simulation|
(Vol , Nr. , In International Conference for High Performance Computing, Networking, Storage and Analysis (SC'10), presented in , pages , , ISSN: , ISBN: , Nov. 2010)
SC10 Best Paper Award
AbstractAlthough system noise is increasingly a concern as HPC systems continue to grow in scale, existing studies with artificial noise models provide only limited insight into application behavior. This paper presents an in-depth analysis of the impact of system noise on large-scale parallel application performance in realistic settings. Our analytical model shows the particular circumstances under which noise is propagated or absorbed. The model shows that not only collective operations but also point-to-point communications influence the application's sensitivity to noise. We present a simulation toolchain that injects noise delays from traces gathered on four common large-scale architectures into a LogGPS simulation and allows new insights into the scaling of applications in noisy environments. We investigate collective operations with up to 1 million processes and three applications (Sweep3D, AMG, and POP) with up to 32.000 processes. We show that the scale at which noise becomes a bottleneck is system-specific and depends on the structure of the noise. Simulations with different network speeds show that a 10x faster network does not improve application scalability because noise becomes a bottleneck at scale. We quantify this noise bottleneck and conclude that our tools can be utilized to tune the noise signatures of a specific system for minimal noise propagation. For example, our simulations verify the long-standing conjecture that co-scheduling prevents significant application slowdown.