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1. ADAPT: An Event-Based Adaptive Collective Communication Framework

   https://doi.org/10.1145/3208040.3208054  

   Luo, Xi; Wu, Wei; Bosilca, George; Patinyasakdikul, Thananon; Wang, Linnan; Dongarra, Jack (June 2018, The 27th International Symposium on High-Performance Parallel and Distributed Computing)

   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. 

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   Corona, Claudia; Ge, Shemin (January 2020, American Geophysical Union Annual Conference)

   null (Ed.)
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   Hood, Wendy R.; Zhang, Yufeng; Taylor, Halie A.; Park, Noel R.; Beatty, Abby E.; Weaver, Ryan J.; Yap, Kang Nian; Kavazis, Andreas N. (June 2019, The Journal of Experimental Biology)
4. GraphPulse: An Event-Driven Hardware Accelerator for Asynchronous Graph Processing

   https://doi.org/10.1109/MICRO50266.2020.00078  

   Rahman, Shafiur; Abu-Ghazaleh, Nael; Gupta, Rajiv (October 2020, 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO))

   null (Ed.)

   Graph processing workloads are memory intensive with irregular access patterns and large memory footprint resulting in low data locality. Their popular software implementations typically employ either Push or Pull style propagation of changes through the graph over multiple iterations that follow the Bulk Synchronous Model. The performance of these algorithms on traditional computing systems is limited by random reads/writes of vertex values, synchronization overheads, and additional overheads for tracking active sets of vertices or edges across iterations. In this paper, we present GraphPulse, a hardware framework for asynchronous graph processing with event-driven scheduling that overcomes the performance limitations of software frameworks. Event-driven computation model enables a parallel dataflow-style execution where atomic updates and active sets tracking are inherent to the model; thus, scheduling complexity is reduced and scalability is enhanced. The dataflow nature of the architecture also reduces random reads of vertex values by carrying the values in the events themselves. We capitalize on the update properties commonly present in graph algorithms to coalesce in-flight events and substantially reduce the event storage requirement and the processing overheads incurred. GraphPulse event-model naturally supports asynchronous graph processing, enabling substantially faster convergence by exploiting available parallelism, reducing work, and eliminating synchronization at iteration boundaries. The framework provides easy to use programming interface for faster development of hardware graph accelerators. A single GraphPulse accelerator achieves up to 74x speedup (28x on average) over Ligra, a state of the art software framework, running on a 12 core CPU. It also achieves an average of 6.2x speedup over Graphicionado, a state of the art graph processing accelerator. 

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5. In the Blink of an Eye: Event-based Emotion Recognition

   https://doi.org/10.1145/3588432.3591511  

   Zhang, Haiwei; Zhang, Jiqing; Dong, Bo; Peers, Pieter; Wu, Wenwei; Wei, Xiaopeng; Heide, Felix; Yang, Xin (July 2023, ACM)