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Title: Scalability under a Power Bound using the GREMLINs Framework

With the move towards exascale, system and software developers will have to deal with issues of extreme parallelism. The system properties affected most by the increase in node and core count are the shared resources on node and across the system. The increase in parallelism leads to reduced memory and bandwidth when regarding individual cores. Since power is a limiting factor for supercomputers, and power is not fully utilized in current systems, overprovisioning compute resources is a viable approach to maximized power utilization. To maximize system performance in regard to these changing conditions, it is necessary to understand how resource restrictions impact performance and system behavior. For the purpose of understanding anticipated system properties the GREMLINs Framework was developed. The framework gives the opportunity to add power restrictions, hinder memory properties and introduce faults to study resilience, among others. These features give the opportunity to use current petascale technology to study problems system designers and software developers will have to face when moving towards exascale and beyond. This work describes the initial release of the GREMLINs Framework, developed for this work, and shows how it can be used to study the scaling behavior of proxy applications. These proxy applications representmore » a selection of HPC workloads important to the scientific community. The proxy applications studied are AMG2013, an algebraic multi-grid linear system solver, CoMD, a classical molecular dynamics proxy application and NEKBONE, an application that uses a high order spectral element method to solve the Navier-Stokes equations. The main interest of these studies lies in analysis regarding their power behavior at scale under a power bound. These findings show how the GREMLINs Framework can help systems and software designers to attain better application performance and can also be used as basis for CPU power balancing tools to use power more efficiently.« less
Authors:
 [1]
  1. Ludwig Maximilian Univ., Munich (Germany)
Publication Date:
OSTI Identifier:
1183550
Report Number(s):
LLNL-TH--667535
TRN: US1601290
DOE Contract Number:
AC52-07NA27344
Resource Type:
Thesis/Dissertation
Research Org:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; NAVIER-STOKES EQUATIONS; COMPUTER CODES; FACE; MOLECULAR DYNAMICS METHOD; PERFORMANCE; SUPERCOMPUTERS; DESIGN; SCALING LAWS; PARALLEL PROCESSING; LIMITING VALUES; POWER DEMAND; ENERGY CONSUMPTION; COMPUTER CALCULATIONS