@article {,
	title = {An international survey on MPI users},
	journal = {Parallel Computing},
	volume = {108},
	year = {2021},
	month = {2021-12},
	abstract = {The Message Passing Interface (MPI) plays a crucial part in the parallel computing ecosystem, a driving force behind many of thehigh-performance computing (HPC) successes. To maintain its relevance to the user community{\textemdash}and in particular to the growingHPC community at large{\textemdash}the MPI standard needs to identify and understand the MPI users{\textquoteright} concerns and expectations, and adaptaccordingly to continue to efficiently bridge the gap between users and hardware.  This questionnaire survey was conducted usingtwo online questionnaire frameworks and has gathered more than 850 answers from 42 countries since February 2019.  Some ofpreceding surveys of MPI uses are questionnaire surveys like ours, while others are conducted either by analyzing MPI programsto reveal static behavior or by using profiling tools to analyze the dynamic runtime behavior of MPI jobs.  Our survey is differentfrom other questionnaire surveys in terms of its larger number of participants and wide geographic spread. As a result, it is possibleto illustrate the current status of MPI users more accurately and with a wider geographical distribution. In this report, we will showsome interesting findings, compare the results with preceding studies when possible, and provide some recommendations for MPIForum based on the findings.},
	keywords = {message passing interface, MPI, survey},
	doi = {10.1016/j.parco.2021.102853},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0167819121000983},
	author = {Atsushi Hori and Emmanuel Jeannot and George Bosilca and Takahiro Ogura and Balazs Gerofi and Jie Yin and Yutaka Ishikawa}
}
@conference {,
	title = {Predicting MPI Collective Communication Performance Using Machine Learning},
	booktitle = {2020 IEEE International Conference on Cluster Computing (CLUSTER)},
	year = {2020},
	month = {2020-09},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Kobe, Japan},
	abstract = {The Message Passing Interface (MPI) defines the semantics of data communication operations, while the implementing libraries provide several parameterized algorithms for each operation. Each algorithm of an MPI collective operation may work best on a particular system and may be dependent on the specific communication problem. Internally, MPI libraries employ heuristics to select the best algorithm for a given communication problem when being called by an MPI application. The majority of MPI libraries allow users to override the default algorithm selection, enabling the tuning of this selection process. The problem then becomes how to select the best possible algorithm for a specific case automatically. In this paper, we address the algorithm selection problem for MPI collective communication operations. To solve this problem, we propose an auto-tuning framework for collective MPI operations based on machine-learning techniques. First, we execute a set of benchmarks of an MPI library and its entire set of collective algorithms. Second, for each algorithm, we fit a performance model by applying regression learners. Last, we use the regression models to predict the best possible (fastest) algorithm for an unseen communication problem. We evaluate our approach for different MPI libraries and several parallel machines. The experimental results show that our approach outperforms the standard algorithm selection heuristics, which are hard-coded into the MPI libraries, by a significant margin.},
	keywords = {Auto-tuning, GAM, KNN, Machine Learning, message passing interface, Performance Prediction, XGBoost},
	doi = {https://doi.org/10.1109/CLUSTER49012.2020.00036},
	author = {Sascha Hunold and Abhinav Bhatele and George Bosilca and Peter Knees}
}
@inproceedings {1309,
	title = {UCX: An Open Source Framework for HPC Network APIs and Beyond},
	journal = {2015 IEEE 23rd Annual Symposium on High-Performance Interconnects},
	year = {2015},
	month = {Aug},
	pages = {40-43},
	publisher = {IEEE},
	address = {Santa Clara, CA, USA},
	abstract = {This paper presents Unified Communication X (UCX), a set of network APIs and their implementations for high throughput computing. UCX comes from the combined effort of national laboratories, industry, and academia to design and implement a high-performing and highly-scalable network stack for next generation applications and systems. UCX design provides the ability to tailor its APIs and network functionality to suit a wide variety of application domains and hardware. We envision these APIs to satisfy the networking needs of many programming models such as Message Passing Interface (MPI), OpenSHMEM, Partitioned Global Address Space (PGAS) languages, task-based paradigms and I/O bound applications. To evaluate the design we implement the APIs and protocols, and measure the performance of overhead-critical network primitives fundamental for implementing many parallel programming models and system libraries. Our results show that the latency, bandwidth, and message rate achieved by the portable UCX prototype is very close to that of the underlying driver. With UCX, we achieved a message exchange latency of 0.89 us, a bandwidth of 6138.5 MB/s, and a message rate of 14 million messages per second. As far as we know, this is the highest bandwidth and message rate achieved by any network stack (publicly known) on this hardware.},
	keywords = {application program interfaces, Bandwidth, Electronics packaging, Hardware, high throughput computing, highly-scalable network stack, HPC, HPC network APIs, I/O bound applications, Infiniband, input-output programs, Libraries, Memory management, message passing, message passing interface, Middleware, MPI, open source framework, OpenSHMEM, parallel programming, parallel programming models, partitioned global address space languages, PGAS, PGAS languages, Programming, protocols, public domain software, RDMA, system libraries, task-based paradigms, UCX, Unified Communication X},
	isbn = {978-1-4673-9160-3},
	doi = {10.1109/HOTI.2015.13},
	author = {P. Shamis and Manjunath Gorentla Venkata and M. Graham Lopez and M. B. Baker and O. Hernandez and Y. Itigin and M. Dubman and G. Shainer and R. L. Graham and L. Liss and Y. Shahar and S. Potluri and D. Rossetti and D. Becker and D. Poole and C. Lamb and S. Kumar and C. Stunkel and George Bosilca and Aurelien Bouteiller}
}
