1. Scientific Computing Goals Past progress Future

2. Goals Numerical algorithms & computational strategies Solve specific set of problems associated with center – – simulation, image processing, meshing Parallel architectures Clusters/MPI Multiprocessors Streaming architectures (GPUs) Deliver new technologies – software infrastructure APIs and end-user applications

3. Activities/Results Shared memory architectures General multithreading capability Linear solvers for PDEs Scriptable capability for exploring parameters spaces GPUs Visualization Volume rendering

4. Graphics Processing Units (GPUs) “Graphics boards” Built primarily for 3D computer games What they do Geometry (polygons) Textures (images) Trends Increased power – – Data types, speed, memory Increased flexibility – – Arithmetic, data types Inexpensive (e.g. $400)

5. Other GPU Activities in SCI Interactive 3D level-set segmentation (NSF) Interactive image segmentation (Exxon Mobil) Diffusion tensor image analysis (NIH) Tomographic/MRI reconstruction (GE, NIH) Sorting algorithms with CUDA (NSF, DOE) Unbiased atlas construction (NSF, NIH) Streaming tetrahedra simplification (NSF, DOE) Unstructured grid volume rendering (DOE) Material point method (MPM) simulations (DOE) Dye-advection for flow visualization (KAUST) Schlieren imaging for flow visualization (DOE) Computing on GPU clusters (NVIDIA,NSF)

6. Planned Activities Simulation Multithreaded work for PDE solvers Clusters/MPI Extend to GPUs Images-to-models N-body interactions – – Particle systems – multiprocessors and GPUs Image analysis – – PDEs, filtering, shortest paths on GPUs Visualization GPUs for rendering, analysis

7. Challenges Shared memory Load balancing Cache performance Limited parallism Clusters Load balancing Communication Shared datastructures (programming models)

8. Challenges GPUs Computational model Efficient memory access (RAM model not efficient) Limited memory and GPU-CPU bandwidth Software Portability, reliability are challenges CUDA could help (NVIDIA only)