@conference {,
	title = {FFT-Based Gradient Sparsification for the Distributed Training of Deep Neural Networks},
	booktitle = {9th International Symposium on High-Performance Parallel and Distributed Computing (HPDC 20)},
	year = {2020},
	month = {2020-06},
	publisher = {ACM},
	organization = {ACM},
	address = {Stockholm, Sweden},
	abstract = {The performance and efficiency of distributed training of Deep Neural Networks (DNN) highly depend on the performance of gradient averaging among participating processes, a step bound by communication costs. There are two major approaches to reduce communication overhead: overlap communications with computations (lossless), or reduce communications (lossy). The lossless solution works well for linear neural architectures, e.g. VGG, AlexNet, but more recent networks such as ResNet and Inception limit the opportunity for such overlapping. Therefore, approaches that reduce the amount of data (lossy) become more suitable. In this paper, we present a novel, explainable lossy method that sparsifies gradients in the frequency domain, in addition to a new range-based float point representation to quantize and further compress gradients. These dynamic techniques strike a balance between compression ratio, accuracy, and computational overhead, and are optimized to maximize performance in heterogeneous environments.

Unlike existing works that strive for a higher compression ratio, we stress the robustness of our methods, and provide guidance to recover accuracy from failures. To achieve this, we prove how the FFT sparsification affects the convergence and accuracy, and show that our method is guaranteed to converge using a diminishing θ in training. Reducing θ can also be used to recover accuracy from the failure. Compared to STOA lossy methods, e.g., QSGD, TernGrad, and Top-k sparsification, our approach incurs less approximation error, thereby better in both the wall-time and accuracy. On an 8 GPUs, InfiniBand interconnected cluster, our techniques effectively accelerate AlexNet training up to 2.26x to the baseline of no compression, and 1.31x to QSGD, 1.25x to Terngrad and 1.47x to Top-K sparsification.},
	keywords = {FFT, Gradient Compression, Loosy Gradients, Machine Learning, Neural Networks},
	doi = {https://doi.org/10.1145/3369583.3392681},
	author = {Linnan Wang and Wei Wu and Junyu Zhang and Hang Liu and George Bosilca and Maurice Herlihy and Rodrigo Fonseca}
}
@conference {,
	title = {Flexible Data Redistribution in a Task-Based Runtime System},
	booktitle = {IEEE International Conference on Cluster Computing (Cluster 2020)},
	year = {2020},
	month = {2020-09},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Kobe, Japan},
	abstract = {Data redistribution aims to reshuffle data to optimize some objective for an algorithm. The objective can be multi-dimensional, such as improving computational load balance or decreasing communication volume or cost, with the ultimate goal to increase the efficiency and therefore decrease the time-to-solution for the algorithm. The classical redistribution problem focuses on optimally scheduling communications when reshuffling data between two regular, usually block-cyclic, data distributions. Recently, task-based runtime systems have gained popularity as a potential candidate to address the programming complexity on the way to exascale. In addition to an increase in portability against complex hardware and software systems, task-based runtime systems have the potential to be able to more easily cope with less-regular data distribution, providing a more balanced computational load during the lifetime of the execution. In this scenario, it becomes paramount to develop a general redistribution algorithm for task-based runtime systems, which could support all types of regular and irregular data distributions. In this paper, we detail a flexible redistribution algorithm, capable of dealing with redistribution problems without constraints of data distribution and data size and implement it in a task-based runtime system, PaRSEC. Performance results show great capability compared to ScaLAPACK, and applications highlight an increased efficiency with little overhead in terms of data distribution and data size.},
	doi = {https://doi.org/10.1109/CLUSTER49012.2020.00032},
	author = {Qinglei Cao and George Bosilca and Wei Wu and Dong Zhong and Aurelien Bouteiller and Jack Dongarra}
}
@conference {,
	title = {HAN: A Hierarchical AutotuNed Collective Communication Framework},
	booktitle = {IEEE Cluster Conference},
	year = {2020},
	month = {2020-09},
	publisher = {Best Paper Award, IEEE Computer Society Press},
	organization = {Best Paper Award, IEEE Computer Society Press},
	address = {Kobe, Japan},
	abstract = {High-performance computing (HPC) systems keep growing in scale and heterogeneity to satisfy the increasing computational need, and this brings new challenges to the design of MPI libraries, especially with regard to collective operations. To address these challenges, we present "HAN," a new hierarchical autotuned collective communication framework in Open MPI, which selects suitable homogeneous collective communication modules as submodules for each hardware level, uses collective operations from the submodules as tasks, and organizes these tasks to perform efficient hierarchical collective operations. With a task-based design, HAN can easily swap out submodules, while keeping tasks intact, to adapt to new hardware. This makes HAN suitable for the current platform and provides a strong and flexible support for future HPC systems. To provide a fast and accurate autotuning mechanism, we present a novel cost model based on benchmarking the tasks instead of a whole collective operation. This method drastically reduces tuning time, as the cost of tasks can be reused across different message sizes, and is more accurate than existing cost models. Our cost analysis suggests the autotuning component can find the optimal configuration in most cases. The evaluation of the HAN framework suggests our design significantly improves the default Open MPI and achieves decent speedups against state-of-the-art MPI implementations on tested applications.},
	author = {Xi Luo and Wei Wu and George Bosilca and Yu Pei and Qinglei Cao and Thananon Patinyasakdikul and Dong Zhong and Jack Dongarra}
}
@conference {,
	title = {Task Bench: A Parameterized Benchmark for Evaluating Parallel Runtime Performance},
	booktitle = {International Conference for High Performance Computing Networking, Storage, and Analysis (SC20)},
	year = {2020},
	month = {2020-11},
	publisher = {ACM},
	organization = {ACM},
	abstract = {We present Task Bench, a parameterized benchmark designed to explore the performance of distributed programming systems under a variety of application scenarios. Task Bench dramatically lowers the barrier to benchmarking and comparing multiple programming systems by making the implementation for a given system orthogonal to the benchmarks themselves: every benchmark constructed with Task Bench runs on every Task Bench implementation. Furthermore, Task Bench{\textquoteright}s parameterization enables a wide variety of benchmark scenarios that distill the key characteristics of larger applications.

To assess the effectiveness and overheads of the tested systems, we introduce a novel metric, minimum effective task granularity (METG). We conduct a comprehensive study with 15 programming systems on up to 256 Haswell nodes of the Cori supercomputer. Running at scale, 100μs-long tasks are the finest granularity that any system runs efficiently with current technologies. We also study each system{\textquoteright}s scalability, ability to hide communication and mitigate load imbalance.},
	url = {https://dl.acm.org/doi/10.5555/3433701.3433783},
	author = {Elliott Slaughter and Wei Wu and Yuankun Fu and Legend Brandenburg and Nicolai Garcia and Wilhem Kautz and Emily Marx and Kaleb S. Morris and Qinglei Cao and George Bosilca and Seema Mirchandaney and Wonchan Lee and Sean Treichler and Patrick McCormick and Alex Aiken}
}
@conference {1205,
	title = {ADAPT: An Event-Based Adaptive Collective Communication Framework},
	booktitle = {The 27th International Symposium on High-Performance Parallel and Distributed Computing (HPDC {\textquoteright}18)},
	year = {2018},
	month = {2018-06},
	publisher = {ACM Press},
	organization = {ACM Press},
	address = {Tempe, Arizona},
	abstract = {The increase in scale and heterogeneity of high-performance computing (HPC) systems predispose the performance of Message Passing Interface (MPI) collective communications to be susceptible to noise, and to adapt to a complex mix of hardware capabilities. The designs of state of the art MPI collectives heavily rely on synchronizations; these designs magnify noise across the participating processes, resulting in significant performance slowdown. Therefore, such design philosophy must be reconsidered to efficiently and robustly run on the large-scale heterogeneous platforms. In this paper, we present ADAPT, a new collective communication framework in Open MPI, using event-driven techniques to morph collective algorithms to heterogeneous environments. The core concept of ADAPT is to relax synchronizations, while mamtaining the minimal data dependencies of MPI collectives. To fully exploit the different bandwidths of data movement lanes in heterogeneous systems, we extend the ADAPT collective framework with a topology-aware communication tree. This removes the boundaries of different hardware topologies while maximizing the speed of data movements. We evaluate our framework with two popular collective operations: broadcast and reduce on both CPU and GPU clusters. Our results demonstrate drastic performance improvements and a strong resistance against noise compared to other state of the art MPI libraries. In particular, we demonstrate at least 1.3X and 1.5X speedup for CPU data and 2X and 10X speedup for GPU data using ADAPT event-based broadcast and reduce operations.},
	isbn = {9781450357852},
	doi = {10.1145/3208040.3208054},
	author = {Xi Luo and Wei Wu and George Bosilca and Thananon Patinyasakdikul and Linnan Wang and Jack Dongarra}
}
@conference {1100,
	title = {Efficient Communications in Training Large Scale Neural Networks},
	booktitle = {ACM MultiMedia Workshop 2017},
	year = {2017},
	month = {2017-10},
	publisher = {ACM},
	organization = {ACM},
	address = {Mountain View, CA},
	abstract = {We consider the problem of how to reduce the cost of communication that is required for the parallel training of a neural network. The state-of-the-art method, Bulk Synchronous Parallel Stochastic Gradient Descent (BSP-SGD), requires many collective communication operations, like broadcasts of parameters or reductions for sub-gradient aggregations, which for large messages quickly dominates overall execution time and limits parallel scalability. To address this problem, we develop a new technique for collective operations, referred to as Linear Pipelining (LP). It is tuned to the message sizes that arise in BSP-SGD, and works effectively on multi-GPU systems. Theoretically, the cost of LP is invariant to P, where P is the number of GPUs, while the cost of more conventional Minimum Spanning Tree (MST) scales like O(logP). LP also demonstrate up to 2x faster bandwidth than Bidirectional Exchange (BE) techniques that are widely adopted by current MPI implementations. We apply these collectives to BSP-SGD, showing that the proposed implementations reduce communication bottlenecks in practice while preserving the attractive convergence properties of BSP-SGD.},
	author = {Yiyang Zhao and Linnan Wan and Wei Wu and George Bosilca and Richard Vuduc and Jinmian Ye and Wenqi Tang and Zenglin Xu}
}
@conference {941,
	title = {GPU-Aware Non-contiguous Data Movement In Open MPI},
	booktitle = {25th International Symposium on High-Performance Parallel and Distributed Computing (HPDC{\textquoteright}16)},
	year = {2016},
	month = {2016-06},
	publisher = {ACM},
	organization = {ACM},
	address = {Kyoto, Japan},
	abstract = {<p>Due to better parallel density and power efficiency, GPUs have become more popular for use in scientific applications. Many of these applications are based on the ubiquitous Message Passing Interface (MPI) programming paradigm, and take advantage of non-contiguous memory layouts to exchange data between processes. However, support for efficient non-contiguous data movements for GPU-resident data is still in its infancy, imposing a negative impact on the overall application performance.</p>

<p>To address this shortcoming, we present a solution where we take advantage of the inherent parallelism in the datatype packing and unpacking operations. We developed a close integration between Open MPI{\textquoteright}s stack-based datatype engine, NVIDIA{\textquoteright}s Unied Memory Architecture and GPUDirect capabilities. In this design the datatype packing and unpacking operations are offloaded onto the GPU and handled by specialized GPU kernels, while the CPU remains the driver for data movements between nodes. By incorporating our design into the Open MPI library we have shown significantly better performance for non-contiguous GPU-resident data transfers on both shared and distributed memory machines.</p>},
	keywords = {datatype, gpu, hybrid architecture, MPI, non-contiguous data},
	doi = {http://dx.doi.org/10.1145/2907294.2907317},
	author = {Wei Wu and George Bosilca and Rolf vandeVaart and Sylvain Jeaugey and Jack Dongarra}
}
@conference {841,
	title = {Hierarchical DAG scheduling for Hybrid Distributed Systems},
	booktitle = {29th IEEE International Parallel \& Distributed Processing Symposium (IPDPS)},
	year = {2015},
	month = {2015-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Hyderabad, India},
	abstract = {Accelerator-enhanced computing platforms have drawn a lot of attention due to their massive peak com-putational capacity. Despite significant advances in the pro-gramming interfaces to such hybrid architectures, traditional programming paradigms struggle mapping the resulting multi-dimensional heterogeneity and the expression of algorithm parallelism, resulting in sub-optimal effective performance. Task-based programming paradigms have the capability to alleviate some of the programming challenges on distributed hybrid many-core architectures. In this paper we take this concept a step further by showing that the potential of task-based programming paradigms can be greatly increased with minimal modification of the underlying runtime combined with the right algorithmic changes. We propose two novel recursive algorithmic variants for one-sided factorizations and describe the changes to the PaRSEC task-scheduling runtime to build a framework where the task granularity is dynamically adjusted to adapt the degree of available parallelism and kernel effi-ciency according to runtime conditions. Based on an extensive set of results we show that, with one-sided factorizations, i.e. Cholesky and QR, a carefully written algorithm, supported by an adaptive tasks-based runtime, is capable of reaching a degree of performance and scalability never achieved before in distributed hybrid environments. },
	keywords = {dense linear algebra, gpu, heterogeneous architecture, PaRSEC runtime},
	author = {Wei Wu and Aurelien Bouteiller and George Bosilca and Mathieu Faverge and Jack Dongarra}
}
