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Accelerating Uintah Components with ARCHES GPU Ray Tracing

This project focuses on leveraging GPUs to accelerate the ARCHES Ray Tracer within the Uintah framework. By offloading tasks to GPUs, the system maximizes hardware utilization and improves performance. The implementation includes a hybrid CPU/GPU scheduler, utilizing Nvidia CUDA for parallelization and high-performance random number generation. The system can efficiently manage tasks, data prefetching, memory allocation, and transfers to fully utilize available CPU cores and GPUs.

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Accelerating Uintah Components with ARCHES GPU Ray Tracing

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  1. ARCHES: GPU Ray Tracing Motivation – Emergence of Heterogeneous Systems Overview and Approach Uintah Hybrid CPU/GPU Scheduler Current Uintah Infrastructure GPU Abilities Acknowledgements: DoE for funding the CSAFE project from 1997-2012, DOE NETL, DOE NNSA, INCITE NSF for funding via SDCI and PetaApps Keeneland Computing Facility, supported by NSF under Contract OCI-0910735 Oak Ridge Leadership Computing Facility – DoE Jaguar XK6 System (GPU partition) http://www.uintah.utah.edu

  2. Emergence of Heterogeneous Systems • Motivation - Accelerate Uintah Components: • ARCHES Ray Tracer • Want to utilize all hardware on these systems • Uintah’s asynchronous task-based approach is well suited to take advantage of GPUs Nvidia M2070/90 Tesla GPU + Multi-core CPU Keeneland Initial Delivery System 360 GPUs DoE Titan 1000s of GPUs

  3. Overview and Approach • ARCHES Ray Tracer: • RayTrace task is computationally intensive • Ideal for parallelization on GPU • Offload Ray Tracing and RNG to GPU(s) • Available CPU cores can perform other computation. • Uintah infrastructure now supports GPU task scheduling and execution: • Can access multiple GPUs on-node • Uses Nvidia CUDA C/C++

  4. Random Number Generation • Using NVIDIA cuRANDLibrary • High performance GPU-accelerated random number generation (RNG)

  5. Uintah Hybrid CPU/GPU Scheduler • Create & schedule CPU & GPU tasks • Enables Uintah to “pre-fetch” GPU data • Uintah infrastructure manages: • Queues of CUDA Stream and Event handles • Device memory allocation and transfers • Utilize all available: • CPU cores and GPUs Keenland IDS - HP SL390 Compute Node

  6. Uintah CPU/GPU Scheduler Design

  7. Uintah GPU Scheduler Abilities • Has now run capability jobs on: • Keeneland Initial Delivery System (NICS) • 1440 CPU cores & 360 GPUs simultaneously • Jaguar - GPU partition (OLCF) • 15360 CPU cores & 960 GPUs simultaneously • Shown speedups on fluid-solver code • Nearing Completion of GPU Ray Tracer Prototype • Will run on systems listed above

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