Joint NHR@ZIB - INTEL oneAPI Workshop

Welcome and Introduction to Day 1 

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oneAPI – Introduction to a new Development Environment
- Concept and oneAPI Standardization initiative
- Intel’s Tools Implementation – Intel oneAPI Toolkits and libs
- Transition from Intel Parallel Studio XE to Intel oneAPI toolkits

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Introduction to the DevCloud
- Purpose: Demoing, testing and porting applications
- Hardware and Software offerings
- How to onboard & how to get an DevCloud account

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Direct programming with oneAPI Compilers – with Demos
- Intro to DPC++ heterogenous programming model and SYCL 2020
- Fortran and offloading with OpenMP5.0
- DPC++ features and examples
   o  “Hello World” Example
   o  Device Selection
   o  Compilation and Execution Flow
   o  Execution Model
   o  Memory Model; Buffers, Unified Shared Memory (USM)
   o  Kernel Model

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Compatibility tool for porting purposes with demo            
- Migration Cuda based GPU Applications to oneAPI (DPC++)

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Intel oneAPI libraries (oneMKL, oneDAL, …)  - with demos
- Performance optimized libraries for AI, numerical simulations and other purposes

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Welcome and Introduction to Day 2 

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A oneAPI Case Study: easyWave - A Tsunami Simulations Application
with demos ZIB's experiences with Intel oneAPI       
- Porting a tsunami application from CUDA to DPC++
- Running DPC++ code on GPU
- From CUDA to DPC++ back to Nvidia GPUs... and FPGAs
- A oneAPI case study with the tsunami simulation easyWave

Live Demos:  Christgau_playbook.tar.gz
(1) overview on CUDA code,
(2) CUDA code execution
(3) guided transformation with Compatibility Tool,
(4) presentation/analysis of generated DPC++ code,
(5) execution on different architectures (Intel CPU + GPU, Nvidia GPU)

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Intel OpenMP – with Demos
- Parallelizing heterogenous applications with Intel OpenMP and OpenMP offloading

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Intel HW (public) roadmap/XPU and architecture specifics
- Server CPUs
- Client CPUs (i.e., Tigerlake; iCore with integrated graphics)
- Intel hardware accelerators GPUs (DG1, SG1, ATS)  and Intel FPGAs

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A oneAPI Case Study: Ginkgo – a sparse linear algebra library for OneAPI Hardware
Experience and potential of using oneAPI for the Ginkgo sparse linear algebra library       
- Porting of numerical linear algebra kernels from Cuda to DPC++
- Pitfalls and solutions in the OneAPI Compatibility Tool
- Performance of Ginkgo’s DPC++ backend on Intel GPU
- Live demo running Ginkgo in the Intel DevCloud 

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Application profiling for heterogenous hardware - Demos
- Profile DPC++ and GPU Workload VTune Profiler and VTune Offload
- Share experiences/key findings  with Gromacs related porting and optimization efforts 

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Application profiling for heterogenous hardware - Demos
- Profile DPC++ and GPU Workload with Advisor include and Roofline analyser
- Estimate performance potential gains with Offload Advisor ( CPU -> HW Accelerator)

Playbook_Advisor_GPU_NBody.txt

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