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Discover the capabilities and challenges of petascale computing for real-time simulations in the life sciences at the Chaste Workshop by University of Oxford. Explore parallel solutions, optimization techniques, benchmarking, and advancements in code performance to propel scientific computing.
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Chaste Workshop University of Oxford HPC and Chaste: Towards Real-Time Simulation James Southern Fujitsu Laboratories of Europe
The petascale challenge • Petascale computing has been achieved in the last 12 months. • 1015 (one million billion) floating point operations per second • There will be many challenges in building and using the next-generation computer systems. • One of the biggest is developing applications, algorithms and numerical libraries that will make efficient use of the computer. • Fujitsu aims to contribute through collaboration with world-leading modelling and simulation groups.
“World’s most powerful supercomputer for science” November 2008 Cray XT5 (Opteron) Oak Ridge The world’s second petascale computer
Petascale computing and the heart • The MEXT Next-Generation Supercomputer Project will provide a ‘leadership’ 10 petaflop/s resource for Japan by 2012. • 10 times faster than the world’s current fastest computer. • A budget of £500 million. • Fujitsu to be one of the main contractors • One of the main application fields to be targeted is the life sciences. • Real-time simulation of a beating heart is a life science application that requires petascale computing.
Parallel solution of the bidomain equations • Current simulation methods are >100,000 times slower than real-time for a realistic mesh on a single processor. • Large scale parallel computing will be needed if we are to realize real-time heart simulations in the near future. • This leads to a series of additional factors that must be considered when solving the bidomain equations. • How do we minimize communication between processors? • How do we ensure each processor does the same amount of work? • It is important that we balance these factors with the scientific computing issues that also have an impact.
Making the code run faster • Improve performance of existing methods. • Profile and benchmark the code to identify parts of the code that could be improved. • Which parts of the code take most of the time? • Are some processors spending a lot of time waiting for others to finish executing code? • Develop new, improved methods. • More efficient solution of the bidomain equations. • Better linear solvers, preconditioners. • Improved ODE solvers. • Getting the same level of accuracy for less computation. • Adaptivity.
Benchmarking and profiling • Benchmarking tells us how fast Chaste is in comparison to similar codes. • Difficult to get a precise measure as we often can’t run exactly the same simulations. • Meshes used by others not available to us, not always possible to get hold of other applications to run comparison. • Indications are that Chaste is very competitive. • Profiling showed that partitioning was causing a load imbalance when we used large meshes. • Some processors doing a lot more work than others. • Solution: re-distribute mesh nodes over the processors by optimizing with METIS library.
Parallel scaling • Good load balancing is key to good parallel performance. • Allows us to minimize the time each processor spends waiting for synchronization with others. • Introducing METIS has significantly improved load balancing in Chaste. • Also important to limit the amount of time processors have to spend communicating with one another. • This time could be better used doing calculations. • How good is Chaste’s parallel performance currently? • For a perfectly load balanced code with no communication overhead we’d expect linear speedup. • i.e. Twice as many processors Half the execution time
Summary • Chaste has been designed to work with HPC. • Should not impact performance on smaller systems. • We regularly benchmark and profile the code both sequentially and in parallel. • Performance improvements can be rapidly implemented to address any issues raised by profiling. • Chaste’s parallel performance is very good (up to at least 32 processors). • We will be working to ensure that this scaling is carried forward onto even larger systems.
Virtual Physiological Human • A flagship eHealth project in FP7. • Computational frameworks and ICT-based tools for multiscale models of the human anatomy, physiology and pathology. • Libraries of data and toolbox for simulation and visualisation. • A key facilitator for: • Personalized (patient-specific) healthcare solutions. • Early diagnosis and predictive medicine. • Assessment of safety/efficacy of drugs using patient-specific computational models. • Innovative personalized drugs.
preDiCT • Collaborative project to create a framework for in silico testing of drugs, using Chaste as the base software. • Systems-based approach, using multi-scale and multi-physics techniques to integrate components from different levels of biological organization. • Partners from high-performance computing, academic institutions and the pharmaceutical industry. • Ultimate goal is to facilitate real time in silico testing of the cardiac toxicity of drugs. • FLE leads the Computational Tools and Methodologies work package of the project.
