@article {,
	title = {Cache Optimization and Performance Modeling of Batched, Small, and Rectangular Matrix Multiplication on Intel, AMD, and Fujitsu Processors},
	journal = {ACM Transactions on Mathematical Software},
	volume = {49},
	year = {2023},
	month = {2023-09},
	pages = {1 - 29},
	abstract = {Factorization and multiplication of dense matrices and tensors are critical, yet extremely expensive pieces of the scientific toolbox. Careful use of low rank approximation can drastically reduce the computation and memory requirements of these operations. In addition to a lower arithmetic complexity, such methods can, by their structure, be designed to efficiently exploit modern hardware architectures. The majority of existing work relies on batched BLAS libraries to handle the computation of many small dense matrices. We show that through careful analysis of the cache utilization, register accumulation using SIMD registers and a redesign of the implementation, one can achieve significantly higher throughput for these types of batched low-rank matrices across a large range of block and batch sizes. We test our algorithm on three CPUs using diverse ISAs {\textendash} the Fujitsu A64FX using ARM SVE, the Intel Xeon 6148 using AVX-512, and AMD EPYC 7502 using AVX-2, and show that our new batching methodology is able to obtain more than twice the throughput of vendor optimized libraries for all CPU architectures and problem sizes.},
	issn = {0098-3500},
	doi = {10.1145/3595178},
	url = {https://dl.acm.org/doi/10.1145/3595178},
	author = {Deshmukh, Sameer and Yokota, Rio and Bosilca, George}
}
@conference {,
	title = {Elastic deep learning through resilient collective operations},
	booktitle = {SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis},
	year = {2023},
	month = {2023-11},
	publisher = {ACM},
	organization = {ACM},
	address = {Denver, CO},
	abstract = {A robust solution that incorporates fault tolerance and elastic scaling capabilities for distributed deep learning. Taking advantage of MPI resilient capabilities, aka. User-Level Failure Mitigation (ULFM), this novel approach promotes efficient and lightweight failure management and encourages smooth scaling in volatile computational settings. The proposed ULFM MPI-centered mechanism outperforms the only officially supported elastic learning framework, Elastic Horovod (using Gloo and NCCL), by a significant factor. These results reinforce the capability of MPI extension to deal with resiliency, and promote ULFM as an effective technique for fault management, minimizing downtime, and thereby enhancing the overall performance of distributed applications, in particular elastic training in high-performance computing (HPC) environments and machine learning applications.},
	isbn = {9798400707858},
	doi = {10.1145/3624062.3626080},
	url = {https://dl.acm.org/doi/abs/10.1145/3624062.3626080},
	author = {Li, Jiali and Bosilca, George and Bouteiller, Aur{\'e}lien and Nicolae, Bogdan}
}
@conference {,
	title = {Performance Insights into Device-initiated RMA Using Kokkos Remote Spaces},
	booktitle = {2023 IEEE International Conference on Cluster Computing Workshops (CLUSTER Workshops)},
	year = {2023},
	month = {2023-11},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Santa Fe, NM, USA},
	abstract = {Achieving scalable performance on supercomputers requires careful coordination of communication and computation. Often, MPI applications rely on buffering, packing, and sorting techniques to accommodate a two-sided API, minimize communication overhead, and achieve performance goals. As interconnects between accelerators become more performant and scalable, programming models such as SHMEM may have the opportunity to enable bandwidth maximization along with ease of programming. In this work, we take a closer look at device-initiated PGAS programming models using NVIDIA Corp{\textquoteright}s NVSHMEM communication library and our interface through the Kokkos Remote Spaces project. We show that benchmarks can benefit from this programming model in terms of performance and programmability. We anticipate similar results for miniapplications.},
	doi = {10.1109/CLUSTERWorkshops61457.2023.00028},
	url = {https://ieeexplore.ieee.org/document/10321871/},
	author = {Mishler, Daniel and Ciesko, Jan and Olivier, Stephen and Bosilca, George}
}
@conference {,
	title = {Reducing Data Motion and Energy Consumption of Geospatial Modeling Applications Using Automated Precision Conversion},
	booktitle = {2023 IEEE International Conference on Cluster Computing (CLUSTER)},
	year = {2023},
	month = {2023-11},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Santa Fe, NM, USA},
	abstract = {The burgeoning interest in large-scale geospatial modeling, particularly within the domains of climate and weather prediction, underscores the concomitant critical importance of accuracy, scalability, and computational speed. Harnessing these complex simulations{\textquoteright} potential, however, necessitates innovative computational strategies, especially considering the increasing volume of data involved. Recent advancements in Graphics Processing Units (GPUs) have opened up new avenues for accelerating these modeling processes. In particular, their efficient utilization necessitates new strategies, such as mixed-precision arithmetic, that can balance the trade-off between computational speed and model accuracy. This paper leverages PaRSEC runtime system and delves into the opportunities provided by mixed-precision arithmetic to expedite large-scale geospatial modeling in heterogeneous environments. By using an automated conversion strategy, our mixed-precision approach significantly improves computational performance (up to 3X) on Summit supercomputer and reduces the associated energy consumption on various Nvidia GPU generations. Importantly, this implementation ensures the requisite accuracy in environmental applications, a critical factor in their operational viability. The findings of this study bear significant implications for future research and development in high-performance computing, underscoring the transformative potential of mixed-precision arithmetic on GPUs in addressing the computational demands of large-scale geospatial modeling and making a stride toward a more sustainable, efficient, and accurate future in large-scale environmental applications.},
	doi = {10.1109/CLUSTER52292.2023.00035},
	url = {https://ieeexplore.ieee.org/document/10319946/},
	author = {Cao, Qinglei and Abdulah, Sameh and Ltaief, Hatem and Genton, Marc G. and Keyes, David and Bosilca, George}
}
@inproceedings {,
	title = {Synchronizing MPI Processes in Space and Time},
	journal = {EUROMPI {\textquoteright}23: 30th European MPI Users{\textquoteright} Group Meeting},
	year = {2023},
	month = {2023-09},
	publisher = {ACM},
	address = {Bristol, United Kingdom},
	abstract = {Performance benchmarks are an integral part of the development and evaluation of parallel algorithms, both in distributed applications as well as MPI implementations themselves. The initial step of the benchmark process is to obtain a common timestamp to mark the start of an operation across all involved processes, and the state-of-the-art in many applications and widely used MPI benchmark suites is the use of MPI barriers. In this paper, we show that the synchronization in space provided by an MPI_Barrier is insufficient for proper benchmark results of parallel distributed algorithms, using MPI collective operations as examples. The resulting lack of a global start timestamp for an operation leads to skewed results, with a significant impact of the used barrier algorithm. In order to mitigate these issues, we propose and discuss the implementation of MPIX_Harmonize, which extends the synchronization in space provided by MPI_Barrier with a time synchronization to guarantee a common starting timestamp across all involved processes. By replacing the use of MPI_Barrier with MPIX_Harmonize, benchmark implementors can eliminate skews resulting from barrier algorithms and achieve stable performance benchmark results. We will show that the proper time synchronization can have significant impact on the benchmark results for various implementations of MPI_Allreduce, MPI_Reduce, and MPI_Bcast.},
	isbn = {9798400709135},
	doi = {10.1145/3615318.3615325},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3615318},
	author = {Schuchart, Joseph and Hunold, Sascha and Bosilca, George}
}
