HPCwire: Beyond Benchmarking

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March 17, 2006

Beyond Benchmarking

by Michael Feldman, Editor, HPCwire

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As the cost of developing, deploying and maintaining high performance systems rises, it becomes more and more important to predict system performance in advance. At Los Alamos National Laboratory (LANL), the Performance and Architecture Lab (PAL), lead by Adolfy Hoisie, is developing advanced modeling techniques to assess current high performance systems as well as understand how future computer architectures will perform. The PAL group is part LANL's Computer & Computational Sciences (CCS) Division, lead by Bill Feiereisen.

PAL researchers have developed a number of accurate models of applications that are important to LANL, as well as its government sponsors - NNSA, DARPA and the DOE Office of Science. They use these models to analyze, predict and calibrate performance for the systems of interest. As the hardware becomes available, they validate their predictions with real-world tests. In this way, PAL's performance modeling can be used to guide system development and procurement decisions.

Adolfy Hoisie, PAL team leader PAL's application models are used to understand the interaction between applications, software environments and computer hardware. The combination of the application workload, the operating system (including the application scheduling environment) and the hardware architecture presents a complex set of criteria for performance modeling.

"As the systems and applications we are using become more and more complex, understanding the interplay between various factors becomes very difficult," explains Adolfy Hoisie, team leader at PAL. "Benchmarking is not cutting it anymore."

In-house at LANL, PAL is able to evaluate a large variety of clusters with different types of processors, interconnects and other hardware components. Cluster systems, such as the Appro HyperBlade, are used for performance analysis of systems and application, performance analysis methodology development, validation and benchmarking and system software analysis and development. The PAL team also has access to supercomputing systems throughout the United States and the rest of the world.

"We've applied our models to analyze performance of many of the very large scale machines in the last decade: ASCI Red, Blue Mountain, ASCI White, ASCI Q, Earth Simulator, ASCI Purple, Blue Gene, Cray X1, etc, and probably tens of clusters of various sizes using most of the microprocessors -- from Intel, AMD, IBM, etc. -- and most of the interconnects on the market -- Myrinet, Quadrics, Infiniband and others," said Hoisie. "We have access to large computing systems virtually anywhere in the world. We've used machines at Sandia, Livermore and NASA as well as European machines. So we are not confined to using machines just in our own backyard."

Hoisie says that their performance modeling work was used to determine that the ASCI Q supercomputer installed at LANL was running at half its potential capacity. By pinpointing the performance degradation causes, they were able to correct the problem. This methodology was since applied to optimize the performance of other large-scale systems. Their models are being used to ensure that some of the latest HPC systems such as Red Storm, Cray X1, and Blue Gene are performing up to their potential.

Once the application models are validated on real hardware, the PAL researchers can use them to predict performance for future systems. For example: what would the application performance on System X be if you doubled the processor speed, quadrupled the memory size, added 50 percent more nodes and increased the network bandwidth by five times? What-if scenarios, such as these, allow engineers to predict how a system may be expanded or redesigned to achieve greater performance. In addition, researchers can modify the application code, itself, to explore algorithm performance dependencies.

Hoisie explains that part of the uniqueness of their approach to performance modeling is that they are able to capture the performance of full applications. LANL is particularly interested in the characteristic applications related to many scientific areas such as global climate modeling, computational biology and astrophysics among others. Some examples of the application workloads used at PAL are the SAIC Adaptive Grid Eulerian (SAGE), Parallel Ocean Program (POP), the Monte-Carlo N-Particle (MCNP), the ocean modeling HYCOM and the shock dynamics CTH codes.

"These [models] are overall predictors of how the whole system performs -- hardware, software and algorithm characteristics," says Bill Feiereisen, division leader for CCS. "So it goes quite a ways beyond benchmarking."

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