Scaling HPC applications
Applies to: ✔️ Linux VMs ✔️ Windows VMs ✔️ Flexible scale sets ✔️ Uniform scale sets
Optimal scale-up and scale-out performance of HPC applications on Azure requires performance tuning and optimization experiments for the specific workload. This section and the VM series-specific pages offer general guidance for scaling your applications.
The azurehpc repo contains many examples of:
- Setting up and running applications optimally.
- Configuration of file systems, and clusters.
- Tutorials on how to get started easily with some common application workflows.
Optimally scaling MPI
The following suggestions apply for optimal application scaling efficiency, performance, and consistency:
For smaller scale jobs (i.e. < 256K connections) use the option:
For larger scale jobs (i.e. > 256K connections) use the option:
In the above, to calculate the number of connections for your MPI job, use:
Max Connections = (processes per node) x (number of nodes per job) x (number of nodes per job)
Adaptive Routing (AR) allows Azure Virtual Machines (VMs) running EDR and HDR InfiniBand to automatically detect and avoid network congestion by dynamically selecting more optimal network paths. As a result, AR offers improved latency and bandwidth on the InfiniBand network, which in turn drives higher performance and scaling efficiency. For more details, refer to the TechCommunity article.
- Pin processes to cores using a sequential pinning approach (as opposed to an autobalance approach).
- Binding by Numa/Core/HwThread is better than default binding.
- For hybrid parallel applications (OpenMP+MPI), use 4 threads and 1 MPI rank per CCX on HB and HBv2 VM sizes.
- For pure MPI applications, experiment with 1-4 MPI ranks per CCX for optimal performance on HB and HBv2 VM sizes.
- Some applications with extreme sensitivity to memory bandwidth may benefit from using a reduced number of cores per CCX. For these applications, using 3 or 2 cores per CCX may reduce memory bandwidth contention and yield higher real-world performance or more consistent scalability. In particular, MPI Allreduce may benefit from this approach.
- For significantly larger scale runs, it is recommended to use UD or hybrid RC+UD transports. Many MPI libraries/runtime libraries do this internally (such as UCX or MVAPICH2). Check your transport configurations for large-scale runs.
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Though not necessary, compiling applications with appropriate optimization flags provides the best scale-up performance on HB and HC-series VMs.
AMD Optimizing C/C++ Compiler
The AMD Optimizing C/C++ Compiler (AOCC) compiler system offers a high level of advanced optimizations, multi-threading, and processor support that includes global optimization, vectorization, inter-procedural analyses, loop transformations, and code generation. AOCC compiler binaries are suitable for Linux systems having GNU C Library (glibc) version 2.17 and above. The compiler suite consists of a C/C++ compiler (clang), a Fortran compiler (FLANG), and a Fortran front end to Clang (Dragon Egg).
Clang is a C, C++, and Objective-C compiler handling preprocessing, parsing, optimization, code generation, assembly, and linking.
Clang supports the
-march=znver1 flag to enable best code generation and tuning for AMD’s Zen based x86 architecture.
The FLANG compiler is a recent addition to the AOCC suite (added April 2018) and is currently in pre-release for developers to download and test. Based on Fortran 2008, AMD extends the GitHub version of FLANG (https://github.com/flang-compiler/flang). The FLANG compiler supports all Clang compiler options and an additional number of FLANG-specific compiler options.
DragonEgg is a gcc plugin that replaces GCC’s optimizers and code generators with those from the LLVM project. DragonEgg that comes with AOCC works with gcc-4.8.x, has been tested for x86-32/x86-64 targets, and has been successfully used on various Linux platforms.
GFortran is the actual frontend for Fortran programs responsible for preprocessing, parsing, and semantic analysis generating the GCC GIMPLE intermediate representation (IR). DragonEgg is a GNU plugin, plugging into GFortran compilation flow. It implements the GNU plugin API. With the plugin architecture, DragonEgg becomes the compiler driver, driving the different phases of compilation. After following the download and installation instructions, Dragon Egg can be invoked using:
$ gfortran [gFortran flags] -fplugin=/path/AOCC-1.2-Compiler/AOCC-1.2- FortranPlugin/dragonegg.so [plugin optimization flags] -c xyz.f90 $ clang -O3 -lgfortran -o xyz xyz.o $./xyz
PGI Community Edition 17 is confirmed to work with AMD EPYC. A PGI-compiled version of STREAM does deliver full memory bandwidth of the platform. The newer Community Edition 18.10 (Nov 2018) should likewise work well. Below is sample CLI to compiler optimally with the Intel Compiler:
pgcc $(OPTIMIZATIONS_PGI) $(STACK) -DSTREAM_ARRAY_SIZE=800000000 stream.c -o stream.pgi
Intel Compiler 18 is confirmed to work with AMD EPYC. Below is sample CLI to compiler optimally with the Intel Compiler.
icc -o stream.intel stream.c -DSTATIC -DSTREAM_ARRAY_SIZE=800000000 -mcmodel=large -shared-intel -Ofast –qopenmp
For HPC, AMD recommends GCC compiler 7.3 or newer. Older versions, such as 4.8.5 included with RHEL/CentOS 7.4, are not recommended. GCC 7.3, and newer, will deliver significantly higher performance on HPL, HPCG, and DGEMM tests.
gcc $(OPTIMIZATIONS) $(OMP) $(STACK) $(STREAM_PARAMETERS) stream.c -o stream.gcc
- Test your knowledge with a learning module on optimizing HPC applications on Azure.
- Review the HBv3-series overview and HC-series overview.
- Read about the latest announcements, HPC workload examples, and performance results at the Azure Compute Tech Community Blogs.
- Learn more about HPC on Azure.