UNM PSAAP talk

content/talk/20210219_unm_psaap/index.md

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+---
+title: Adding Fast GPU Derived Datatype Handing to Existing MPIs
+
+date: "2021-02-15T00:00:00Z"
+
+tags: [mpi]
+---
+
+*February 19th, 1:00 PM, University of New Mexico, Albuquerque, NM*
+
+Invited talk to the PSAAP Colloquium at University of New Mexico Computer Science
+
+### Abstract
+
+MPI derived datatypes are an abstraction that simplifies handling of non-contiguous data in MPI applications. These datatypes are recursively constructed at runtime from primitive Named Types defined in the MPI standard. More recently, the development and deployment of CUDA-aware MPI implementations has encouraged the transition of distributed high-performance MPI codes to use GPUs. Such implementations allow MPI functions to directly operate on GPU buffers, easing integration of GPU compute into MPI codes. Despite substantial attention to CUDA-aware MPI implementations, they continue to offer cripplingly poor GPU performance when manipulating derived datatypes on GPUs. This work presents a new MPI library, TEMPI, to address this issue. TEMPI first introduces a common datatype to represent equivalent MPI derived datatypes. TEMPI can be used as an interposed library on existing MPI deployments without system or application changes. Furthermore, this work presents a performance model of GPU derived datatype handling, demonstrating that previously preferred “one-shot” methods are not always fastest.
+
+### Link
+
+* [slides]()
+* [github](https://github.com/cwpearson/tempi)
+* [pdf](/pdf/20210121_pearson_arxiv.pdf)
+* [arxiv](https://arxiv.org/abs/2012.14363)