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Abstract

With the end of Dennard scaling, specializing and distributing compute engines throughout the system is a promising technique to improve applications performance. For example, NVIDIA's BlueField Data Processing Unit (DPU) integrates programmable processing elements within the network and offers specialized network processing capabilities. These capabilities enable communication via offloads onto DPUs and present new application opportunities for offloading nonblocking or complex communication patterns such as collective communication operations. This paper discusses the lessons learned enabling DPU-based acceleration for collective communication algorithms by describing the impact of such offloaded collective operations on two applications: Octopus and P3DFFT++. We present new algorithms for the nonblocking MPI_Ialltoallv and blocking MPI_Allgatherv collective operations that leverage DPU offloading, which are used by the above applications, and evaluate them. Our experiments show a performance improvement in the range of 14% to 49% for P3DFFT++ and 17% for Octopus, even though the performance of those collectives in well-balanced OSU latency benchmarks shows comparable performance to well-optimized host-based implementations of these collectives. This demonstrates that taking into account load imbalance in communication algorithms can help improve application performance where such imbalance is common and large in magnitude.