2019 BenchCouncil International Symposium on Benchmarking, Measuring and Optimizing (Bench'19)
Denver, Colorado, USA Nov 14-16, 2019
2019 BenchCouncil International Symposium on Benchmarking, Measuring and Optimizing (Bench'19)
Denver, Colorado, USA Nov 14-16, 2019
Go to schedule for: Thursday, Friday, Saturday, Program Details
The Best Paper Award will be accompanied by a prize of $1,000.
Bench'19 Location: Copper 1
Registration: 7:30am - 17:00pm
| Time | Event |
|---|---|
| 07:30-17:00 | Registration @ Copper 1 meeting room |
| 08:35-08:45 | Opening Remarks (Dr. Dan Stanzione & Dr. Xiaoyi Lu) |
| 08:45-09:15 | BenchCouncil: Present and Future [pdf] Prof. Jianfeng Zhan |
| 09:15-09:55 | Keynote 1: Benchmarking Supercomputers in the Post-Moore era [pdf]
Dr. Dan Stanzione, Associate Vice President for Research at The University of Texas at Austin  Abstract: In this talk, we will cover the increasing gaps between headline performance and application performance on Frontera and the last several generations of TACC supercomputers. We will also discuss the challenges of developing a new benchmark suite for the upcoming Leadership-Class Computing Facility, and solicit community input on capability benchmarks.
Bio: Dr. Dan Stanzione, Associate Vice President for Research at The University of Texas at Austin since 2018 and Executive Director of the Texas Advanced Computing Center (TACC) since 2014, is a nationally recognized leader in high performance computing. He is the principal investigator (PI) for a National Science Foundation (NSF) grant to deploy Frontera, which is the fastest supercomputer at any U.S. university. Stanzione is also the PI of TACC's Stampede2 and Wrangler systems, supercomputers for high performance computing and for data-focused applications, respectively. For six years he was co-PI of CyVerse, a large-scale NSF life sciences cyberinfrastructure. Stanzione was also a co-PI for TACC's Ranger and Lonestar supercomputers, large-scale NSF systems previously deployed at UT Austin. Stanzione received his bachelor's degree in electrical engineering and his master's degree and doctorate in computer engineering from Clemson University. |
| 09:55-10:10 | Coffee Break |
| 10:10-11:00 | Best Paper Session I (2 regular papers) Chair: Tianshu Hao (ICT, CAS) Early Experience in Benchmarking Edge AI Processors with Object Detection Workloads [pdf] by Yujie Hui (The Ohio State University), Jeffrey Lien (NovuMind Inc.) and Xiaoyi Lu (The Ohio State University) GraphBench: A Benchmark Suite for Graph Computing Systems [pdf] by Lei Wang and Minghe Yu (Institute of computing technology, Chinese Academy of Sciences) |
| 11:00-11:50 | Session I: Scientific Computing (2 regular papers) Chair: Tianshu Hao (ICT, CAS) Apache Spark Streaming, Kafka and HarmonicIO: A Performance Benchmark and Architecture Comparison for Enterprise and Scientific Computing [pdf] by Ben Blamey, Andreas Hellander and Salman Zubair Toor (Uppsala University ) Benchmark researches from the perspective of Metrology by Kun Yang, Tong Wu and Qingfei Shen (National Institute of Metrology) |
| 12:00-13:30 | Lunch @ Copper 2 |
| 13:30-14:00 | Chair: Pravin Chandran (Intel) Invited Talk I: FloraBench:an end-to-end application benchmark suite for datacenter [pdf] Dr. Zheng Cao, Alibaba |
| 14:00-14:50 | Session II: Performance Analysis (2 regular papers) Chair: Pravin Chandran (Intel) NTP : A Neural Net Topology Profiler [pdf] by Pravin Chandran (Intel), Raghavendra Bhat (Intel), Juby Jose (Intel), Viswanath Dibbur (Ex Intel) and Prakash Sirra Ajith (Ex Sasken) MCC: a Predictable and Scalable Massive Client Load Generator [pdf] by Wenqing Wu, Xiao Feng, Wenli Zhang and Mingyu Chen (ICT, Chinese Academy of Sciences; University of Chinese Academy of Sciences) |
| 14:50-15:05 | Coffee Break |
| 15:05-16:20 | Best Paper Session II (3 papers) Chair: Xiaoyi Lu (The Ohio State University) Performance Analysis of GPU Programming Models using the Roofline Scaling Trajectories [pdf] by Khaled Ibrahim, Samuel Williams and Leonid Oliker (Lawrence Berkeley National Laboratory ) Anomaly Analysis and Diagnosis for Co-located Datacenter Workloads in the Alibaba Cluster [pdf] by Rui Ren (Institute of computing technology, Chinese Academy of Sciences) SSH-Backed API Performance Case Study [pdf] by Anagha Jamthe, Mike Packard, Joe Stubbs (Texas Advanced Computing Center, Austin TX), Gilbert Curbelo (California State University of Monterey Bay, Marina CA), Roseline Shapi (Mississippi Valley State University, Itta Bena, MS) and Elias Chalhoub (The University of Texas at Austin) |
| 16:20-17:10 | Session III: Benchmark (2 regular papers) Chair: Xiaoyi Lu (The Ohio State University) Building the DataBench Workflow and Architecture [pdf] by Todor Ivanov, Timo Eichhorn (Goethe University Frankfurt) and Arne Berre (SINTEF AS) Benchmarking Solvers for The One Dimensional Cubic Nonlinear Klein Gordon Equation on a Single Core [pdf] by Benson Muite (University of Tartu) and Samar Aseeri (KAUST) |
| Time | Event |
|---|---|
| 07:30-17:00 | Registration @ Copper 1 meeting room |
| 08:35-09:15 | Chair: khaled lbrahim (Lawrence Berkeley National Laboratory) Keynote 2: Benchmarks and Middleware for Designing Convergent HPC, Big Data and Deep Learning Software Stacks for Exascale Systems [pdf] Prof. Dhabaleswar K. (DK) Panda, OSU  Abstract: This talk will focus on challenges in designing benchmarks and middleware for convergent HPC, Deep Learning, and Big Data Analytics Software stacks for Exascale systems with millions of processors and accelerators. For the HPC domain, we will discuss about the OSU Micro-Benchmarks (OMB) Suite and associated middleware for designing runtime environments for MPI+X programming models by taking into account support for multi-core systems (x86, OpenPOWER, and ARM), high-performance networks, and GPGPUs (including GPUDirect RDMA. Features and sample performance numbers from the MVAPICH2 libraries (http://mvapich.cse.ohio-state.edu) will be presented. An overview of RDMA-based designs for Hadoop (HDFS, MapReduce, RPC and HBase), Spark, and Memcached, together with the OSU HiBD benchmarks (http://hibd.cse.ohio-state.edu) will be presented for Big Data Analytics. For the Deep Learning domain, we will focus on a set of different benchmarks and profiling tools to deliver scalable DNN training with Horovod and TensorFlow using MVAPICH2-GDR MPI library (http://hidl.cse.ohio-state.edu).
Bio: Dhabaleswar K. (DK) Panda is a Professor and University Distinguished Scholar of Computer Science and Engineering at the Ohio State University. He has published over 450 papers in the area of high-end computing and networking. The MVAPICH2 (High Performance MPI and PGAS over InfiniBand, Omni-Path, iWARP and RoCE) libraries, designed and developed by his research group (http://mvapich.cse.ohio-state.edu), are currently being used by more than 3,025 organizations worldwide (in 89 countries). More than 600,000 downloads of this software have taken place from the project's site. This software is empowering several InfiniBand clusters (including the 3rd, 5th, 8th, 15th, 16th, 19th, and 31st ranked ones) in the TOP500 list. The RDMA packages for Apache Spark, Apache Hadoop and Memcached together with OSU HiBD benchmarks from his group (http://hibd.cse.ohio-state.edu) are also publicly available. These libraries are currently being used by more than 315 organizations in 35 countries. More than 31,300 downloads of these libraries have taken place. High-performance and scalable versions of the Caffe and TensorFlow framework are available from https://hidl.cse.ohio-state.edu. Prof. Panda is an IEEE Fellow. More details about Prof. Panda are available at http://www.cse.ohio-state.edu/~panda. |
| 09:15-09:45 | Chair: khaled lbrahim (Lawrence Berkeley National Laboratory) Invited Talk II: Towards Benchmarking AIOT Device based on MCU [pdf] by Dr Dong Li |
| 09:45-10:00 | Coffee Break |
| 10:00-10:50 | Session IV: Big Data (2 regular papers) Chair: khaled lbrahim (Lawrence Berkeley National Laboratory) Benchmarking Database Ingestion Ability with Real-Time Big Astronomical Data [pdf] by Qing Tang, Chen Yang, Xiaofeng Meng (Renmin University) and Zhihui Du (Tsinghua University) A Practical Data Repository for Causal Learning with Big Data [pdf] by Lu Cheng (Arizona State University), Ruocheng Guo (Arizona State University), Raha Moraffah (Arizona State University), K.S. Candan (Arizona State University), Adrienne Raglin (US Army Research Laboratory) and Huan Liu (Arizona State University) |
| 10:50-12:00 | Challenge Session I(4 talks) Chair: Jianfeng Zhan (ICT, CAS) Track 1: International AI System Challenge based on RISC-V RVTensor: A light-weight neural network inference framework based on the RISC-V architecture [pdf] by Yu Jiageng (Institute of Software, CAS) Track 2: International AI System Challenge based on Cambricon Track XDN: Towards Efficient Inference of Residual Neural Networks on Cambricon Chips [pdf] by Guangli Li (Institute of Computing Technology, CAS) Track 3: International AI System Challenge based on X86 Platform Exploiting Parallelism, Sparsity and Locality to Accelerate ALS-WR on x86 Platforms [pdf] by Pengyu Wang (Shanghai Jiao Tong University) Track 4: International 3D Face Recognition Algorithm Challenge Improving RGB-D face recognition via transfer learning from a pretrained 2D network [pdf] by Xingwang Xiong (Institute of Computing Technology, CAS) |
| 12:00-13:20 | Lunch |
| 13:20-14:00 | Chair: Xiaoyi Lu (The Ohio State University) Keynote 3: Harmonizing High-Level Abstraction and High Performance for Graph Mining Prof. Bo Wu, Colorado School of Mines  Abstract: Graph mining algorithms that aim at identifying structural patterns in graphs are typically more complex than graph computation algorithms such as breadth first search. Researchers have implemented several systems with high-level and flexible interfaces customized for tackling graph mining problems. However, we found that for triangle counting, one of the simplest graph mining problems, such systems can be several times slower than a single-threaded implementation of a straightforward algorithm. In this talk, I will reveal the root causes of the severe inefficiency of state-of-the-art graph mining systems and the challenges to address the performance problems. I will describe AutoMine, a system we developed to automatically generate both specialized algorithms and high-performance low-level code for arbitrary patterns.
Bio: Bo Wu is an Associate Professor in the Department of Computer Science at Colorado School of Mines. His research focuses on leveraging compiler and runtime techniques to build efficient software systems for large-scale graph analytics and machine learning applications on heterogeneous platforms. He received the best paper award at SC’15, an NSF CRII Award, an NSF Early Career Award, and an NSF SPX Award. |
| 14:00-14:30 | Chair: Xiaoyi Lu (The Ohio State University) Invited Talk III: Deep Learning on HPC: performance factors and lessons learned [pdf] Dr. Weijia Xu, University of Texas at Austin  Abstract: In this talk, we report several ongoing efforts for deploying and running deep learning applications using high performance computing clusters at Texas Advanced Computing Center. From both lessons learned through practices and designed experiments, we discuss several factors affecting the deep learning performances, both accuracy and execution time, at various stages of analysis pipeline from low level data storage to high level deep learning framework. The talk will end with discussions and future outlooks on development, deployment and benchmark deep learning applications at scale.
Bio: Dr. Weijia Xu is a research scientist and lead the Scalable Computational Intelligence group at Texas Advanced Computing Center at the University of Texas at Austin. He received his Ph.D. from Computer Science Department at UT Austin and has been an experienced data scientist. Dr. Xu's main research interest is to enable data-driven discoveries through developing new computational methods and applications that facilitate the data-to-knowledge transfer process. Dr. xu leads the group that supports large scale data driven analysis and machine learning applications using computing resources at TACC. His projects have been funded through various federal and state agencies including NIH, NSF, City of Austin, and USDA. He has served in program committees for several workshops and conferences in Big Data, Cloud Computing and HPC areas. |
| 14:30-14:45 | Coffee Break |
| 14:45-15:10 | Session V: Data Center (1 regular papers) Chair: Tianxi Li (The Ohio State University) LCIO: Large Scale Filesystem Aging [pdf] by Matthew Bachstein (University of Tennessee, Knoxville), Feiyi Wang and Sarp Oral (Oak Ridge National Laboratory) |
| 15:10-16:25 | Session VI: AI and Edge (3 regular papers) Chair: Tianxi Li (The Ohio State University) Deep Reinforcement Learning for Auto-optimization of I/O Accelerator Parameters by Trong-Ton Pham and Dennis Djan (Bull - ATOS technologies) Causal Learning in Question Quality Improvement [pdf] by Yichuan Li, Ruocheng Guo, Weiying Wang and Huan Liu (Arizona State University) SparkAIBench: A Benchmark to Generate AI Workloads on Spark [pdf] by Zifeng Liu, Xiaojiang Zuo, Zeqing Li and Rui Han (Beijing institute of technology) |
| 16:25-17:55 | Tutorial: AIBench, Edge benchmark & HPC AI500 Chair: Tianxi Li (The Ohio State University) |
| 16:25-16:55 | AIBench: An Industry Standard AI Benchmark Suite [pdf]
By Dr. Wanling Gao, Fei Tang, ICT, CAS |
| 16:55-17:25 | Edge AIBench: Towards Comprehensive End-to-End Edge [pdf]
By Tianshu Hao, ICT, CAS |
| 17:25-17:55 | HPC AI500: A Benchmark Suite for HPC AI Systems [pdf]
By Zihan Jiang, ICT, CAS |
| 18:15 | Banquet (on-site)
Award Ceremony (Prof. Jianfeng Zhan, Dr. Dan Stanzione, Prof. Geoffrey Fox, & Prof. D.K. Panda) BenchCouncil Achievement Award Lecture Best paper award. BenchCouncil Contribution Award Lecture Challenges Awards |
| Time | Event |
|---|---|
| 07:30-17:00 | Registration @ Copper 1 meeting room |
| 08:40-09:20 | BenchCouncil Achievement Award Presentation
By Prof. Tony Hey  Bio: Tony Hey has a doctorate in particle physics from the University of Oxford. After a career in physics that included research positions at Caltech, MIT and CERN, and a professorship at the University of Southampton, he became interested in parallel computing and moved into computer science. His group was one of the first to build and explore the development of parallel software for message-passing distributed memory computers. He was one of the authors of the first draft of the MPI message-passing standard. Tony led the U.K.’s eScience initiative in 2001 before joining Microsoft in 2005 as Vice-President for Technical Computing. He returned to work in the UK in 2015 as Chief Data Scientist at the Rutherford Appleton Laboratory and leads the ‘Scientific Machine Learning’ group. Tony is a fellow of the Association for Computing Machinery, the American Association for the Advancement of Science, and the Royal Academy of Engineering. |
| 09:20-10:00 | Chair: Xiaoyi Lu (The Ohio State University) Keynote 4: InfiniBand In-Network Computing Technology for Scalable HPC/AI Dr. Gilad Shainer, Senior VP, Mellanox Abstract: The ever increasing demands for higher computation performance drive the creation of new datacenter accelerators and processing units. Previously CPUs and GPUs were the main sources for compute power. The exponential increase in data volume and in problems complexity, drove the creation of a new processing unit – the I/O processing unit or IPU. IPUs are interconnect elements that include In-Network Computing engines, engines that can participate in the application run time, and analyze application data as it being transferred within the data center, or at the edge. The combination of CPUs, GPUs, and IPUs, creates the next generation of data center and edge computing architectures. The first generations of IPUs are already in use in leading HPC and Deep learning data centers, have been integrated into multiple MPI frameworks, NVIDIA NCCL, Charm++ and others, and have demonstrated accelerate performance by nearly 10X. Bio: Gilad Shainer serves as Mellanox's senior vice president of marketing, focusing on high- performance computing. Mr. Shainer joined Mellanox in 2001 as a design engineer and later served in senior marketing management roles since 2005. Mr. Shainer serves as the chairman of the HPC-AI Advisory Council organization, he serves as the president of UCF and CCIX consortiums, a board member in the OpenCAPI and OpenFabrics organizations, a member of IBTA and contributor to the PCISIG PCI-X and PCIe specifications. Mr. Shainer holds multiple patents in the field of high-speed networking. He is a recipient of 2015 R&D100 award for his contribution to the CORE-Direct In-Network Computing technology and the 2019 R&D100 award for his contribution to the UCX technology. Gilad Shainer holds MSc degree and BSc degree in Electrical Engineering from the Technion Institute of Technology in Israel. |
| 10:00-10:15 | Coffee Break |
| 10:15-10:45 | Chair: Ben Blamey (Uppsala University) Invited Talk IV: Towards a Methodology for Benchmarking Edge Processing Frameworks [pdf] Dr. Gabriel Antoniu, INRIA  Abstract: With the spectacular growth of the Internet of Things, edge processing emerged as a relevant means to offload data processing and analytics from centralized Clouds to the devices that serve as data sources (often provided with some processing capabilities). While a large plethora of frameworks for edge processing were recently proposed, the distributed systems community has no clear means today to discriminate between them. Some preliminary surveys exist, focusing on a feature-based comparison. We claim that a step further is needed, to enable a performance-based comparison. To this purpose, the definition of a benchmark is a necessity. In this talk, we make this step by discussing the definition of a methodology for benchmarking Edge processing frameworks.
Bio: Gabriel Antoniu is a Senior Research Scientist at Inria, Rennes. He leads the KerData research team, focusing on storage and I/O management for Big Data processing on scalable infrastructures (clouds, HPC systems). His main current interests regard HPC-Big Data convergence for data storage and processing aspects. He currently serves as Vice Executive Director of JLESC – Joint Inria- Illinois- ANL-BSC-JSC-RIKEN/AICS Laboratory for Extreme-Scale Computing on behalf of Inria. He received his Ph.D. degree in Computer Science in 2001 from ENS Lyon. He leads several international projects in partnership with Microsoft Research, IBM, Argonne National Lab, the University of Illinois at Urbana Champaign, Huawei. He served as Program Chair for the IEEE Cluster conference in 2014 and 2017 and regularly serves as a PC member of major conferences in the area of HPC, cloud computing and Big Data (SC, HPDC, CCGRID, Cluster, Big Data, etc.). He has acted as advisor for 19 PhD theses and has co-authored over 140 international publications in the aforementioned areas. |
| 10:45-11:25 | Chair: Ben Blamey (Uppsala University) Keynote 5: Benchmarking Perspectives on emerging HPC workloads [pdf] Prof. Geoffrey Fox, Indiana University, APS and ACM Fellow  Bio: Geoffrey Charles Fox (https://www.engineering.indiana.edu/,
http://www.dsc.soic.indiana.edu/, gcf@indiana.edu)
Fox received a Ph.D. in Theoretical Physics from Cambridge University where he was Senior
Wrangler. He is now a distinguished professor of Engineering, Computing, and Physics at Indiana
University where he is director of the Digital Science Center. He previously held positions at
Caltech, Syracuse University, and Florida State University after being a postdoc at the Institute for
Advanced Study at Princeton, Lawrence Berkeley Laboratory, and Peterhouse College Cambridge.
He has supervised the Ph.D. of 73 students and published around 1300 papers (over 500 with at
least ten citations) in physics and computing with an hindex of 78 and over 35000 citations. He is
a Fellow of APS (Physics) and ACM (Computing) and works on the interdisciplinary interface
between computing and applications. Current work is in Biology, Pathology, Sensor Clouds and
Ice-sheet Science, Image processing, Deep Learning and Particle Physics. His architecture work is
built around High-performance computing enhanced Software Defined Big Data Systems on
Clouds and Clusters. The analytics focuses on scalable parallel machine learning. He is an expert
on streaming data and robot-cloud interactions. He is involved in several projects to enhance the
capabilities of Minority Serving Institutions. He has experience in online education and its use in
MOOCs for areas like Data and Computational Science
|
| 11:25-12:05 | Chair: Ben Blamey (Uppsala University) Keynote 6: Lightweight Requirements Engineering for Exascale Co-design [pdf] Prof. Felix Wolf, Department of Computer Science of Technische Universität Darmstadt in Germany 
Abstract: Given the tremendous cost of an exascale system, its architecture must match the requirements of the applications it is supposed to run as precisely as possible. Conversely, applications must be designed such that building an appropriate system becomes feasible, motivating the idea of co-design. In this process, a fundamental aspect of the application requirements are the rates at which the demands for different resources grow as a code is scaled to a larger machine. However, if the anticipated scale exceeds the size of available platforms this demand can no longer be measured. This is clearly the case when designing an exascale system. Moreover, creating analytical models to predict these requirements is often too laborious—especially when the number and complexity of target applications is high. In this paper, we show how automated performance modeling can be used to quickly predict application requirements for varying scales and problem sizes. Bio: Felix Wolf is full professor at the Department of Computer Science of Technische Universität Darmstadt in Germany, where he leads the Laboratory for Parallel Programming. He works on methods, tools, and algorithms that support the development and deployment of parallel software systems in various stages of their life cycle. Prof. Wolf received his Ph.D. degree from RWTH Aachen University in 2003. After working more than two years as a postdoc at the Innovative Computing Laboratory of the University of Tennessee, he was appointed research group leader at Jülich Supercomputing Centre. Between 2009 and 2015, he was head of the Laboratory for Parallel Programming at the German Research School for Simulation Sciences in Aachen and full professor at RWTH Aachen University. Prof. Wolf has published more than a hundred refereed articles on parallel computing, several of which have received awards. |
| 12:05-12:10 | Closing Remarks |
Length of Presentations (including Q&A):
Keynotes: 40 minutes
Invited Talks: 30 Minutes
Regular Papers: 25 minutes
Challenge talks : (TBD)
Early Experience in Benchmarking Edge AI Processors with Object Detection Workloads by Yujie Hui (The Ohio State University), Jeffrey Lien (NovuMind Inc.) and Xiaoyi Lu (The Ohio State University)
GraphBench: A Benchmark Suite for Graph Computing Systems by Lei Wang and Minghe Yu (Institute of computing technology, Chinese Academy of Sciences)
Performance Analysis of GPU Programming Models using the Roofline Scaling Trajectories by Khaled Ibrahim, Samuel Williams and Leonid Oliker (Lawrence Berkeley National Laboratory )
Anomaly Analysis and Diagnosis for Co-located Datacenter Workloads in the Alibaba Cluster by Rui Ren (Institute of computing technology, Chinese Academy of Sciences)
SSH-Backed API Performance Case Study by Anagha Jamthe, Mike Packard, Joe Stubbs (Texas Advanced Computing Center, Austin TX), Gilbert Curbelo (California State University of Monterey Bay, Marina CA), Roseline Shapi (Mississippi Valley State University, Itta Bena, MS) and Elias Chalhoub (The University of Texas at Austin)
Apache Spark Streaming, Kafka and HarmonicIO: A Performance Benchmark and Architecture Comparison for Enterprise and Scientific Computing by Ben Blamey, Andreas Hellander and Salman Zubair Toor (Uppsala University )
Benchmark researches from the perspective of Metrology by Kun Yang, Tong Wu and Qingfei Shen (National Institute of Metrology)
NTP : A Neural Net Topology Profiler by Pravin Chandran (Intel), Raghavendra Bhat (Intel), Juby Jose (Intel), Viswanath Dibbur (Ex Intel) and Prakash Sirra Ajith (Ex Sasken)
MCC: a Predictable and Scalable Massive Client Load Generator by Wenqing Wu, Xiao Feng, Wenli Zhang and Mingyu Chen (ICT, Chinese Academy of Sciences; University of Chinese Academy of Sciences)
Building the DataBench Workflow and Architecture by Todor Ivanov, Timo Eichhorn (Goethe University Frankfurt) and Arne Berre (SINTEF AS)
Benchmarking Solvers for The One Dimensional Cubic Nonlinear Klein Gordon Equation on a Single Core by Benson Muite (University of Tartu) and Samar Aseeri (KAUST)
Benchmarking Database Ingestion Ability with Real-Time Big Astronomical Data by Qing Tang, Chen Yang, Xiaofeng Meng (Renmin University) and Zhihui Du (Tsinghua University)
A Practical Data Repository for Causal Learning with Big Data by Lu Cheng (Arizona State University), Ruocheng Guo (Arizona State University), Raha Moraffah (Arizona State University), K.S. Candan (Arizona State University), Adrienne Raglin (US Army Research Laboratory) and Huan Liu (Arizona State University)
LCIO: Large Scale Filesystem Aging by Matthew Bachstein (University of Tennessee, Knoxville), Feiyi Wang and Sarp Oral (Oak Ridge National Laboratory)
Deep Reinforcement Learning for Auto-optimization of I/O Accelerator Parameters by Trong-Ton Pham and Dennis Djan (Bull - ATOS technologies)
Causal Learning in Question Quality Improvement by Yichuan Li, Ruocheng Guo, Weiying Wang and Huan Liu (Arizona State University)
SparkAIBench: A Benchmark to Generate AI Workloads on Spark by Zifeng Liu, Xiaojiang Zuo, Zeqing Li and Rui Han (Beijing institute of technology)