Publications

HAM: Hotspot-Aware Manager for Improving Communications with 3D-Stacked Memory

Published in IEEE Transactions on Computers, 2021

In this article, we propose a novel Hotspot-Aware Manager (HAM) infrastructure for 3D-stacked memory devices capable of optimizing memory access streams via request aggregation, hotspot detection, and in-memory prefetching. We present the HAM design and implementation, and simulate it on a system using RISC-V embedded cores with attached HMC devices. We extensively evaluate HAM with over 12 benchmarks and applications representing diverse irregular memory access patterns. The results show that, on average, HAM reduces redundant requests by 37.51 percent and increases the prefetch buffer hit rate by 4.2 times, compared to a baseline streaming prefetcher. On the selected benchmark set, HAM provides performance gains of 21.81 percent in average (up to 34.28 percent), and power savings of 35.07 percent over a standard 3D-stacked memory.

Recommended citation: Wang, Xi, Antonino Tumeo, John D. Leidel, Jie Li, and Yong Chen. "HAM: Hotspot-Aware Manager for Improving Communications with 3D-Stacked Memory." IEEE Transactions on Computers 70, no. 6 (2021): 833-848. https://artlands.github.io/files/wang-tc-2021.pdf

MonSTer: an out-of-the-box monitoring tool for high performance computing systems

Published in IEEE International Conference on Cluster Computing (CLUSTER), 2020

In this work, we introduce MonSTer, an “out-of-the-box” monitoring tool for high-performance computing platforms. MonSTer uses the evolving specification Redfish to retrieve sensor data from Baseboard Management Controller (BMC), and resource management tools such as Univa Grid Engine (UGE) or Slurm to obtain application information and resource usage data. Additionally, it also uses a time-series database (e.g. InfluxDB) for data storage.

Recommended citation: Li, Jie, Ghazanfar Ali, Ngan V. T. Nguyen, Jon Hass, Alan Sill, Tommy Dang and Yong Chen. “MonSTer: An Out-of-the-Box Monitoring Tool for High Performance Computing Systems.” 2020 IEEE International Conference on Cluster Computing (CLUSTER) (2020): 119-129. https://artlands.github.io/files/li-cluster-2020.pdf

RadarViewer: Visualizing the dynamics of multivariate data

Published in Practice and Experience in Advanced Research Computing (PEARC), 2020

This showcase presents a visual approach based on clustering and superimposing to construct a high-level overview of sequential event data while balancing the amount of information and the cardinality in it. We also implement an interactive prototype, called RadarViewer, that allows domain analysts to simultaneously analyze sequence clustering, extract useful distribution patterns, drill multiple levels-of-detail to accelerate the analysis. The RadarViewer is demonstrated through case studies with real-world temporal datasets of different sizes.

Recommended citation: Nguyen, Ngan, Jon Hass, Yong Chen, Jie Li, Alan Sill, and Tommy Dang. "RadarViewer: Visualizing the dynamics of multivariate data." In Practice and Experience in Advanced Research Computing, pp. 555-556. 2020. https://artlands.github.io/files/ngan-pearc-2020.pdf

Mtsad: Multivariate time series abnormality detection and visualization

Published in IEEE International Conference on Big Data (Big Data), 2019

This paper introduces an approach to analyzing and visualizing highdimensional time series, focusing on identifying multivariate observations that are significantly different from the others. We also propose a prototype, called MTSAD, to guide users when interactively exploring abnormalities in large time series.

Recommended citation: Pham, Vung, Ngan Nguyen, Jie Li, Jon Hass, Yong Chen, and Tommy Dang. "Mtsad: Multivariate time series abnormality detection and visualization." In 2019 IEEE International Conference on Big Data (Big Data), pp. 3267-3276. IEEE, 2019. https://artlands.github.io/files/pham-bigdata-2019.pdf

PIMS: a lightweight processing-in-memory accelerator for stencil computations

Published in Proceedings of the International Symposium on Memory Systems, 2019

In this paper we present PIMS, an in-memory accelerator for stencil computations. PIMS, implemented in the logic layer of a 3D stacked memory, exploits the high bandwidth provided by through silicon vias to reduce redundant memory traffic. Our comprehensive evaluation using three different grid sizes with six categories of orders indicate that the proposed architecture reduces 48.25% of data movement on average and obtains up to 65.55% of bank conflict reduction.

Recommended citation: Li, Jie, Xi Wang, Antonino Tumeo, Brody Williams, John D. Leidel, and Yong Chen. "Pims: a lightweight processing-in-memory accelerator for stencil computations." In Proceedings of the International Symposium on Memory Systems, pp. 41-52. 2019. https://artlands.github.io/files/li-memsys-2019.pdf

MAC: Memory access coalescer for 3D-stacked memory

Published in Proceedings of the 48th International Conference on Parallel Processing, 2019

In this paper we propose MAC (Memory Access Coalescer), a coalescing unit for the 3D-stacked memory. We discuss the design and implementation of MAC, in the context of a custom designed cache-less architecture targeted at data-intensive, irregular applications. Through a custom simulation infrastructure based on the RISC-V toolchain, we show that MAC achieves a coalescing efficiency of 52.85% on average. It improves the performance of the memory system by 60.73% on average for a large set of irregular workloads.

Recommended citation: Wang, Xi, Antonino Tumeo, John D. Leidel, Jie Li, and Yong Chen. "MAC: Memory access coalescer for 3D-stacked memory." In Proceedings of the 48th International Conference on Parallel Processing, pp. 1-10. 2019. https://artlands.github.io/files/wang-icpp-2019.pdf