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| # Intel Hard- and Software | |
| ### 7 Levels of Parallelism | |
| 1. Node level | |
| 2. Socket level | |
| - NUMA | |
| - 2-, 4-, or 8-socket | |
| 3. Hyperthreading: 2 logical threads on one core | |
| - less cache per thread | |
| - useful with jobs utilizing different parts of core - disable on HPC | |
| - now not much of a penalty when enabled | |
| 4. Mesh | |
| 5. GPU-CPU | |
| - subslice = core (8 on Intel Graphics Gen 11) | |
| - exec-unit = thread (8) | |
| 6. Instruction level: out-of order execution on different ports/execution units (max 4 IPC [instructions per cycle]) | |
| 7. Data parallelism | |
| <!---  | |
|  | |
| ---> | |
| ### Vectorization | |
|  | |
| - Performance libraries | |
| - Compiler (fully automized) | |
| - Compiler with vectorization hints (`#pragma`) | |
| - user mandated vectorization (SIMD directive) | |
| - SIMD intrinsic dlass (`F32vec4 add`) | |
| - Vector intrinsic (`mm_add_ps()`) | |
| - assembler code (`addps) | |
| ### oneAPI: New Foundation for Exascale Computing | |
| - unified memory (CPU/GPU) | |
| - all-to-all connectivity | |
| ### Summary | |
| - Code modernization not always easy (analyze & optimize) | |
| - data / task parallelism | |
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| ### Parallel Studio | |
| transition to **oneAPI** in 2020 | |
| in 2020 version | |
| - VNNI (Vector Neural Network inference) for AI inference speedup | |
| - persistent memory (Optane) compatible with RAM | |
| - expanded standard support | |
| - Fortran 2018 | |
| - C++ 17 (20 in initial stage) | |
| - move to LLVM (slowly), backend switchable | |
| - Extended Coarse Grain Profiling | |
| - HPC cloud support | |
| - New OS support (e.g., Amazon) | |
| #### Compiler (v19.0) | |
| #### Python | |
| Take advantage of Intel's python distribution: | |
| [add Intel channel for *anaconda*](https://software.intel.com/en-us/articles/using-intel-distribution-for-python-with-anaconda) ([in general](https://software.intel.com/en-us/distribution-for-python)) | |
| Speedup achieved by optimizing | |
| - *numba* (utilizing MKL) | |
| - *scikit learn* (utilizing [DAAL](http://software.intel.com/daal)) | |
| ##### URLs | |
| - [Installation](https://software.intel.com/en-us/articles/using-intel-distribution-for-python-with-anaconda) | |
| - [Anaconda packages](https://anaconda.org/intel/repo) | |
| ### Performance tools | |
| - VTune: HPC tuning - now even broader coverage (demo in afternoon) | |
| - [performance snapshot](intel.com/performance-snapshot) for high level view | |
| - [Advisor](software.intel.com/advisor) provides information on | |
| - threads | |
| - vectorization | |
| - GPU (offload advisor, coming; so far only Intel *gen9* or *gen11*) | |
| ### [MPI library](software.intel.com/intel-mpi-library) | |
| ### Intel cluster checker | |
| ### Vectorization issues | |
| - Default: compile to early 2000s CPU - only vector length 2 => need compiler flag(s) for better optimization | |
| - technical bit: when using AVX instructions clock frequency is reduced to compensate for extra power consumption | |
| #### Example *N*-body problem | |
| Convert Array of Structures (AoS) -> Structure of Arrays (SoA) for better aligned memory access | |
| --- | |
| ## [oneAPI](https://software.intel.com/en-us/oneapi) | |
| open standard for unified programming model across hardware platforms | |
|  | |
| **C++** (11) + **SYCL** + **Extensions** | |
| ### API libraries | |
| - Math | |
| - Analytics/ML | |
| - DNN | |
| - ... | |
| ### oneAPI Toolkits | |
| - Base | |
| - currently beta | |
| - direct programming (DPC++) | |
| - HPC | |
| - DL | |
| - Rendering | |
| - OpenVINO | |
| - AI Analytics | |
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| [Developer Access](http://software.intel.com/devcloud/oneapi) | |
| GPU target currently **OpenCL-based**, unclear about NVidia support | |
| - https://software.intel.com/en-us/oneapi | |
| - https://github.com/intel/llvm | |
| ## Impressions on the way home | |
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