ADVANCED COMPUTING SYSTEMS

We research innovative architectures and technologies to advance high-performance computing (HPC) systems. The team works across the entire system design process to identify and nurture high-value technologies and architectures, create rapid development capabilities, and deliver advanced prototypes to support the needs of future computing systems.

High performance computing (HPC) systems are growing increasingly complex. The error rate of computation is growing and faults are becoming harder to diagnose and correct. Resilience develops methods to keep applications running to a correct solution in spite of errors. Probabilistic computing provides a mechanism to recover the correct solution even if there are incorrect intermediate results.  Our resilience and probabilistic computing team researches methods of using non-determinism to solve problems faster and more efficiently by eliminating the need for traditional fault tolerance.

The productivity and data analytics office researches new computer architecture and programming models for data intensive problems which are difficult to solve on modern but conventional computer systems. The focus includes high performance tensor-based knowledge discovery, streaming analytics, and run time system research.

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The development of technology and tools to model and simulate proposed computer architectures and examine emerging components is essential to the creation of high performance computing (HPC) systems that balances performance with power consumption, reliability and cost. Modeling, Simulation and Emulation researchers actively guide and cultivate a community of researchers creating such capabilities.

The Neuromorphic Computation Research Program (NCRP) explores the benefits of neuromorphic computation for advanced machine learning frameworks and applyies them to national security missions. NCRP researches advanced hardware architectures to identify the most power-efficient methods of computing machine learning algorithms. Longer-term research includes identifying how the brain computes information to determine if these concepts can be incorporated into neuromorphic processor designs.

At almost all scales, from hand-held devices to large supercomputers, modern computing systems are power limited. The amount of computing that can be performed on a chip, in a cabinet, or in a room is limited by power dissipation. The energy efficient team studies technologies and architectures of high-performance computers to minimize power consumption while maximizing performance.