The school will focus on the theme of Scientific Software for Heterogeneous Architectures. The complete programme will offer 28 hours of lectures and hands-on exercises, and a student presentations session.
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Introduction lecture
Preparing for the HL-LHC computational challenge
by Danilo Piparo (CERN)- HEP data processing and analysis workflows
- Upgrades of the LHC accelerator and experiments
- Evolution of hardware and computing infrastructure
- Impact on HEP data processing software
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Track 1: Technologies and Platforms
4 hours of lectures and 4 hours of hands-on exercises
by Andrzej Nowak
Introduction to efficient computing- The evolution of computing hardware and what it means in practice
- The seven dimensions of performance
- Controlling and benchmarking your computer and software
- Software that scales with the hardware
- Advanced performance tuning in hardware
Hardware evolution and heterogeneity
- Accelerators, co-processors, heterogeneity
- Memory architectures, hardware caching and NUMA
- Compute devices: CPU, GPU, FPGA, ASIC etc.
- The role of compilers
Data-oriented design
- Hardware vectorization in detail – theory vs. practice
- Software design for vectorization and smooth data flow
- How can compilers and other tools help?
Summary and future technologies overview
- Teaching program summary and wrap-up
- Next-generation memory technologies and interconnect
- Future computing evolution
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Track 2: Parallel and Optimised Scientific Software
4 hours of lectures and 5 hours of hands-on exercises
by Sebastien Ponce (CERN)
Writing parallel software- Amdahl's and Gustafson's laws
- Asynchronous execution
- Finding concurrency, task vs. data parallelism
- Using threading in C++ and Python, comparison with multi-process
- Resource protection and thread safety
- Locks, thread local storage, atomic operations
Modern programming languages for HEP
- Why Python and C++?
- Recent evolutions: C++ 11/14/17
- Modern features of C++ related to performance
- Templating versus inheritance, pros and cons of virtual inheritance
- Python 3, and switching from Python 2
Optimizing existing large codebase
- Measuring performance, tools and key indicators
- Improving memory handling
- The nightmare of thread safety
- Code modernization and low level optimizations
- Data structures for efficient computation in modern C++
Practical vectorization
- Measuring vectorization level
- What to expect from vectorization
- Preparing code for vectorization
- Vectorizing techniques in C++: intrinsics, libraries, autovectorization
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Track 3: Programming for Heterogeneous Architectures
4 hours of lectures and 6 hours of hands-on exercises
by Dorothea vom Bruch (CPPM/CNRS)
and Daniel Campora (University of Maastricht)
Scientific computing on heterogeneous architectures (D. vom Bruch)- Introduction to heterogeneous architectures and the performance challenge
- From general to specialized: Hardware accelerators and applications
- Type of workloads ideal for different accelerators
- Trade-offs between multi-core and many-core architectures
- Implications of heterogeneous hardware on the design and architecture of scientific software
- Embarrassingly parallel scientific applications in HPC and CERN
Programming for GPUs (D. vom Bruch)
- From SIMD to SPMD, a programming model transition
- Thread and memory organization
- Basic building blocks of a GPU program
- Control flow, synchronization, atomics
Performant programming for GPUs (D. Campora)
- Data locality, coalesced memory accesses, tiled data processing
- GPU streams, pipelined memory transfers
- Under the hood: branchless, warps, masked execution
- Debugging and profiling a GPU application
Design patterns and best practices (D. Campora)
- Good practices: single precision, floating point rounding, avoid register spilling, prefer single source
- Other standards: SYCL, HIP, OpenCL
- Middleware libraries and cross-architecture compatibility
- Reusable parallel design patterns with real-life applications
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Additional lectures
Student lightning talks session
