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+---
+author: [Richard]
+date: 2025-09-10 
+slug: EESSI-on-Cray-Slingshot
+---
+
+# MPI at Warp Speed: EESSI Meets Slingshot-11
+
+High-performance computing environments are constantly evolving, and keeping pace with the latest interconnect technologies is crucial for maximising application performance. However, we cannot rebuild all the software in EESSI that depends on improvements to communication libraries. So how do we take advantage of new technological developments?
+
+Specifically we look at taking benefit of the HPE/Cray Slingshot-11.
+Slingshot-11 promises to offer a significant advancement in HPC networking, offering improved bandwidth, lower latency, and better scalability for exascale computing workloads ... so
+this should be worth the effort!
+
+In this blog post, we present the requirements for building OpenMPI 5.x with Slingshot-11 support on HPE/Cray systems and its integration with EESSI using the [host_injections](../../../../site_specific_config/host_injections.md)
+mechanism of EESSI to inject custom-built OpenMPI libraries. This approach enables overriding EESSI’s default MPI library with an ABI-compatible, Slingshot-optimized version which should give us optimal performance.
+<!-- more -->
+
+## The Challenge
+
+EESSI provides a comprehensive software stack, but specialized interconnect support like Slingshot-11 can sometimes require custom-built libraries that aren't yet available in the standard EESSI distribution. Our goal is to:
+
+1. Build OpenMPI 5.x with native Slingshot-11 support
+2. Create ABI-compatible replacements for EESSI's OpenMPI libraries
+3. Place the libraries somewhere where EESSI automatically picks them up
+4. Support both x86_64 AMD CPU partitions and NVIDIA Grace CPU partitions with Hopper accelerators
+
+The main task is to build the required dependencies on top of EESSI, since many of the libraries needed for libfabric with CXI support are not yet available in the current EESSI stack.
+
+### System Architecture
+
+Our target system is [Olivia](https://documentation.sigma2.no/hpc_machines/olivia.html#olivia) which is based on HPE Cray EX platforms for compute and accelerator nodes, and HPE Cray ClusterStor for global storage, all
+connected via HPE Slingshot high-speed interconnect.
+It consists of two main distinct partitions:
+
+- **Partition 1**: x86_64 AMD CPUs without accelerators
+- **Partition 2**: NVIDIA Grace CPUs with Hopper accelerators
+
+For the Grace/Hopper partition we also need to enable CUDA support in libfabric.
+
+## Building the Dependency Chain
+
+### Building Strategy
+
+Rather than relying on Cray-provided system packages, we opted to build all dependencies from source [on top of EESSI](../../../../using_eessi/building_on_eessi.md). This approach provides several advantages:
+
+- **Consistency**: All libraries built with the same compiler toolchain
+- **Compatibility**: Ensures ABI compatibility with EESSI libraries
+- **Control**: Full control over build configurations and optimizations
+
+### Required Dependencies
+
+To build OpenMPI 5.x with libfabric and CXI support, we needed the following missing dependencies:
+
+1. **libuv** - Asynchronous I/O library
+2. **libnl** - Netlink library for network configuration
+3. **libconfig** - Library designed for processing structured configuration files
+4. **libfuse** - Filesystem in Userspace library  
+5. **libpdap** - Performance Data Access Protocol library
+6. **shs-libcxi** - Slingshot CXI library
+7. **lm-sensors** - Monitoring tools and drivers
+8. **libfabric 2.x** - OpenFabrics Interfaces library with CXI provider
+9. **OpenMPI 5.x** - The final MPI implementation
+
+## EESSI Integration via `host_injections`
+
+EESSI's `host_injections` mechanism allows us to override EESSI's MPI library with an ABI compatible host MPI while maintaining compatibility with the rest of the software stack. We just need to make sure that the libraries are in the right
+location to be automatically picked up by the software shipped with EESSI. This location is EESSI-version specific, for `2023.06`, with the NVIDIA Grace architecture, that location is:
+```
+/cvmfs/software.eessi.io/host_injections/2023.06/software/linux/aarch64/nvidia/grace/rpath_overrides/OpenMPI/system/lib
+```
+
+**OpenMPI/5.0.7 on ARM nodes built with:** 
+```
+./configure --prefix=/cluster/installations/eessi/default/aarch64/software/OpenMPI/5.0.7-GCC-12.3.0 --with-cuda=${EBROOTCUDA} --with-cuda-libdir=${EBROOTCUDA}/lib64 --with-slurm --enable-mpi-ext=cuda --with-libfabric=${EBROOTLIBFABRIC} --with-ucx=${EBROOTUCX} --enable-mpirun-prefix-by-default  --enable-shared --with-hwloc=/cvmfs/software.eessi.io/versions/2023.06/software/linux/aarch64/nvidia/grace/software/hwloc/2.9.1-GCCcore-12.3.0  --with-libevent=/cvmfs/software.eessi.io/versions/2023.06/software/linux/aarch64/nvidia/grace/software/libevent/2.1.12-GCCcore-12.3.0  --with-pmix=/cvmfs/software.eessi.io/versions/2023.06/software/linux/aarch64/nvidia/grace/software/PMIx/4.2.4-GCCcore-12.3.0  --with-ucc=/cvmfs/software.eessi.io/versions/2023.06/software/linux/aarch64/nvidia/grace/software/UCC/1.2.0-GCCcore-12.3.0  --with-prrte=internal
+```
+### Testing
+
+We plan to provide more comprehensive test results in the future. In this blog post we want to report that the approach works in principle, and that the EESSI stack can pick up and use the custom OpenMPI build and extract
+performance from the host interconnect **without the need to rebuild any software packages**.
+
+**1- Test using OSU-Micro-Benchmarks from EESSI on 2-nodes (x86_64 AMD-CPUs)**:
+```
+Environment set up to use EESSI (2023.06), have fun!
+
+hostname:
+x1001c6s2b0n1
+x1001c6s3b0n0
+
+CPU info:
+Vendor ID:                            AuthenticAMD
+Model name:                           AMD EPYC 9745 128-Core Processor
+Virtualization:                       AMD-V
+
+Currently Loaded Modules:
+  1) GCCcore/12.3.0
+  2) GCC/12.3.0
+  3) numactl/2.0.16-GCCcore-12.3.0
+  4) libxml2/2.11.4-GCCcore-12.3.0
+  5) libpciaccess/0.17-GCCcore-12.3.0
+  6) hwloc/2.9.1-GCCcore-12.3.0
+  7) OpenSSL/1.1
+  8) libevent/2.1.12-GCCcore-12.3.0
+  9) UCX/1.14.1-GCCcore-12.3.0
+ 10) libfabric/1.18.0-GCCcore-12.3.0
+ 11) PMIx/4.2.4-GCCcore-12.3.0
+ 12) UCC/1.2.0-GCCcore-12.3.0
+ 13) OpenMPI/4.1.5-GCC-12.3.0
+ 14) gompi/2023a
+ 15) OSU-Micro-Benchmarks/7.1-1-gompi-2023a
+
+# OSU MPI Bi-Directional Bandwidth Test v7.1
+# Size      Bandwidth (MB/s)
+# Datatype: MPI_CHAR.
+1                       2.87
+2                       5.77
+4                      11.55
+8                      23.18
+16                     46.27
+32                     92.64
+64                    185.21
+128                   369.03
+256                   743.08
+512                  1487.21
+1024                 2975.75
+2048                 5928.14
+4096                11809.66
+8192                23097.44
+16384               31009.54
+32768               36493.20
+65536               40164.63
+131072              43150.62
+262144              45075.57
+524288              45918.07
+1048576             46313.37
+2097152             46507.25
+4194304             46609.10
+
+
+# OSU MPI Latency Test v7.1
+# Size          Latency (us)
+# Datatype: MPI_CHAR.
+1                       1.66
+2                       1.65
+4                       1.65
+8                       1.65
+16                      1.65
+32                      1.65
+64                      1.65
+128                     2.13
+256                     2.20
+512                     2.23
+1024                    2.31
+2048                    2.46
+4096                    2.61
+8192                    2.87
+16384                   3.24
+32768                   5.24
+65536                   6.60
+131072                  9.29
+262144                 14.69
+524288                 26.21
+1048576                47.32
+2097152                90.79
+4194304               182.30
+```
+
+**2- Test using OSU-Micro-Benchmarks/7.5-gompi-2023b-CUDA-12.4.0 from EESSI on 2-nodes/2-GPUs (Grace/Hopper GPUs)**:
+```
+Environment set up to use EESSI (2023.06), have fun!
+
+hostname:
+x1000c4s4b1n0
+x1000c5s3b0n0
+
+CPU info:
+Vendor ID:                            ARM
+
+Currently Loaded Modules:
+  1) GCCcore/13.2.0
+  2) GCC/13.2.0
+  3) numactl/2.0.16-GCCcore-13.2.0
+  4) libxml2/2.11.5-GCCcore-13.2.0
+  5) libpciaccess/0.17-GCCcore-13.2.0
+  6) hwloc/2.9.2-GCCcore-13.2.0
+  7) OpenSSL/1.1
+  8) libevent/2.1.12-GCCcore-13.2.0
+  9) UCX/1.15.0-GCCcore-13.2.0
+ 10) libfabric/1.19.0-GCCcore-13.2.0
+ 11) PMIx/4.2.6-GCCcore-13.2.0
+ 12) UCC/1.2.0-GCCcore-13.2.0
+ 13) OpenMPI/4.1.6-GCC-13.2.0
+ 14) gompi/2023b
+ 15) GDRCopy/2.4-GCCcore-13.2.0
+ 16) UCX-CUDA/1.15.0-GCCcore-13.2.0-CUDA-12.4.0       (g)
+ 17) NCCL/2.20.5-GCCcore-13.2.0-CUDA-12.4.0           (g)
+ 18) UCC-CUDA/1.2.0-GCCcore-13.2.0-CUDA-12.4.0        (g)
+ 19) OSU-Micro-Benchmarks/7.5-gompi-2023b-CUDA-12.4.0 (g)
+
+  Where:
+   g:  built for GPU
+
+# OSU MPI-CUDA Bi-Directional Bandwidth Test v7.5
+# Datatype: MPI_CHAR.
+# Size      Bandwidth (MB/s)
+1                       0.18
+2                       0.37
+4                       0.75
+8                       1.49
+16                      2.99
+32                      5.93
+64                     11.88
+128                    23.76
+256                    72.78
+512                   145.45
+1024                  282.03
+2048                  535.46
+4096                 1020.24
+8192                16477.70
+16384               25982.96
+32768               30728.30
+65536               37637.46
+131072              41808.92
+262144              44316.19
+524288              43693.89
+1048576             43759.66
+2097152             43593.38
+4194304             43436.60
+
+
+# OSU MPI-CUDA Latency Test v7.5
+# Datatype: MPI_CHAR.
+# Size       Avg Latency(us)
+1                      11.71
+2                      11.66
+4                      11.66
+8                      11.71
+16                     11.67
+32                     11.68
+64                     11.66
+128                    12.45
+256                     3.76
+512                     3.82
+1024                    3.91
+2048                    4.08
+4096                    4.25
+8192                    4.49
+16384                   5.09
+32768                   8.02
+65536                   9.56
+131072                 13.52
+262144                 17.96
+524288                 28.94
+1048576                50.50
+2097152                93.98
+4194304               180.14
+```
+
+**3- Test using OSU-Micro-Benchmarks/7.5 with PrgEnv-cray on 2-nodes/2-GPUs (Grace/Hopper GPUs)**:
+```
+
+hostname:
+x1000c4s4b1n0
+x1000c5s3b0n0
+
+CPU info:
+Vendor ID:                            ARM
+
+Currently Loaded Modules:
+  1) craype-arm-grace                      8) craype/2.7.34
+  2) libfabric/1.22.0                      9) cray-dsmml/0.3.1
+  3) craype-network-ofi                   10) cray-mpich/8.1.32
+  4) perftools-base/25.03.0               11) cray-libsci/25.03.0
+  5) xpmem/2.11.3-1.3_gdbda01a1eb3d       12) PrgEnv-cray/8.6.0
+  6) cce/19.0.0                           13) cudatoolkit/24.11_12.6
+
+# OSU MPI-CUDA Bi-Directional Bandwidth Test v7.5
+# Datatype: MPI_CHAR.
+# Size      Bandwidth (MB/s)
+1                       1.06
+2                       2.17
+4                       4.40
+8                       8.80
+16                     17.64
+32                     35.17
+64                     70.55
+128                   140.91
+256                   281.22
+512                   559.04
+1024                 1114.45
+2048                 2081.25
+4096                 4068.64
+8192                 1852.11
+16384               18564.47
+32768               22647.40
+65536               33108.03
+131072              39553.95
+262144              43140.01
+524288              44853.40
+1048576             45761.69
+2097152             46228.10
+4194304             46470.29
+
+# OSU MPI-CUDA Latency Test v7.5
+# Datatype: MPI_CHAR.
+# Size       Avg Latency(us)
+1                       2.76
+2                       2.72
+4                       2.90
+8                       2.86
+16                      2.85
+32                      2.73
+64                      2.60
+128                     3.41
+256                     4.17
+512                     4.19
+1024                    4.29
+2048                    4.44
+4096                    4.66
+8192                    7.59
+16384                   8.17
+32768                   8.44
+65536                   9.92
+131072                 12.59
+262144                 18.07
+524288                 29.00
+1048576                50.64
+2097152                94.06
+4194304               180.44
+```
+
+![OSU CUDA Bandwidth](osu_cuda_bandwidth.png)  ![OSU CUDA Latency](osu_cuda_latency.png) 
+
+## Conclusion
+The approach demonstrates EESSI's flexibility in accommodating specialized hardware requirements while preserving the benefits of a standardized software stack! There is plenty of more testing to do, but the signs at this stage are very good!