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ORCIDISPASS 2021: Stony Brook, NY, USA
- IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS 2021, Stony Brook, NY, USA, March 28-30, 2021. IEEE 2021, ISBN 978-1-7281-8643-6
Paper Session I: Benchmarking
- Arun Subramaniyan, Yufeng Gu, Timothy Dunn, Somnath Paul, Md. Vasimuddin, Sanchit Misra, David T. Blaauw, Satish Narayanasamy
, Reetuparna Das:
GenomicsBench: A Benchmark Suite for Genomics. 1-12 - Trinayan Baruah, Kaustubh Shivdikar, Shi Dong, Yifan Sun, Saiful A. Mojumder, Kihoon Jung, José L. Abellán, Yash Ukidave, Ajay Joshi, John Kim, David R. Kaeli:
GNNMark: A Benchmark Suite to Characterize Graph Neural Network Training on GPUs. 13-23 - Fei Tang, Wanling Gao, Jianfeng Zhan, Chuanxin Lan, Xu Wen, Lei Wang, Chunjie Luo, Zheng Cao, Xingwang Xiong, Zihan Jiang, Tianshu Hao, Fanda Fan, Fan Zhang, Yunyou Huang, Jianan Chen, Mengjia Du, Rui Ren, Chen Zheng, Daoyi Zheng, Haoning Tang, Kunlin Zhan, Biao Wang, Defei Kong, Minghe Yu, Chongkang Tan, Huan Li, Xinhui Tian, Yatao Li, Junchao Shao, Zhenyu Wang, Xiaoyu Wang, Jiahui Dai, Hainan Ye:
AIBench Training: Balanced Industry-Standard AI Training Benchmarking. 24-35
Paper Session II: GPUs
- Petros Anastasiadis, Nikela Papadopoulou, Georgios I. Goumas, Nectarios Koziris:
CoCoPeLia: Communication-Computation Overlap Prediction for Efficient Linear Algebra on GPUs. 36-47 - Atefeh Mehrabi, Donghyuk Lee, Niladrish Chatterjee, Daniel J. Sorin, Benjamin C. Lee, Mike O'Connor:
Learning Sparse Matrix Row Permutations for Efficient SpMM on GPU Architectures. 48-58 - Shougang Yuan, Ardhi Wiratama Baskara Yudha, Yan Solihin, Huiyang Zhou:
Analyzing Secure Memory Architecture for GPUs. 59-69
Poster Session A
- Gokul Subramanian Ravi, Ramon Bertran, Pradip Bose, Mikko H. Lipasti:
MicroGrad: A Centralized Framework for Workload Cloning and Stress Testing. 70-72 - Navneet Raju, Rahul M. Koushik, Hari Om, Subramaniam Kalambur:
ViStA: Video Streaming and Analytics Benchmark. 73-75 - Suyash Bakshi, S. Lennart Johnsson:
Analysis of Factors Affecting Power Consumption and Energy Efficiency of SGEMM on the Low-Power Myriad-2 VPU. 76-78 - Pete Ehrett, Nathan Block, Bing Schaefer, Adrian Berding, John Paul Koenig, Pranav Srinivasan, Valeria Bertacco, Todd M. Austin:
A Defense-Inspired Benchmark Suite. 79-80 - Sri Harsha Gade, Anup Gangwar, Ambica Prasad, Nitin Kumar Agarwal, Ravishankar Sreedharan:
An Automated Traffic Generation Framework for Performance Evaluation of Networks-on-Chip for Real World Use Cases. 81-83 - Mohsen Koohi Esfahani, Peter Kilpatrick, Hans Vandierendonck:
How Do Graph Relabeling Algorithms Improve Memory Locality? 84-86 - Emir C. Marangoz, Kyoung-Don Kang, Seunghee Shin:
Designing GPU Architecture for Memory Bandwidth Reservation. 87-89 - Wei Zhang, Wei Wei, Wen Wang, Lingling Jin, Zheng Cao:
Reducing BERT Computation by Padding Removal and Curriculum Learning. 90-92 - Qi Pei, Seunghee Shin:
Efficient Split Counter Mode Encryption for NVM. 93-95
Paper Session III: Characterization
- Michael Buch, Zahra Azad, Ajay Joshi, Vijay Janapa Reddi:
AI Tax in Mobile SoCs: End-to-end Performance Analysis of Machine Learning in Smartphones. 96-106 - Aniket Anand Deshmukh, Ruihao Li, Rathijit Sen, Robert R. Henry, Monica Beckwith, Gagan Gupta:
Performance Characterization of .NET Benchmarks. 107-117 - Junwei Wu, Jingwei Sun, Hao Sun, Guangzhong Sun:
Performance Analysis of Graph Neural Network Frameworks. 118-127
Paper Session IV: Software Analysis
- Ali Mustafa Zaidi, Konstantinos Iordanou, Mikel Luján, Giacomo Gabrielli:
Loopapalooza: Investigating Limits of Loop-Level Parallelism with a Compiler-Driven Approach. 128-138 - Bryan Harris, Michael Marzullo, Nihat Altiparmak:
Real-Time Characterization of Data Access Correlations. 139-150 - Tarek Ramadan, Tanzima Z. Islam, Chase Phelps, Nathan Pinnow, Jayaraman J. Thiagarajan:
Comparative Code Structure Analysis using Deep Learning for Performance Prediction. 151-161
Paper Session V: Best Paper Nominations
- Michael Lui, Yavuz Yetim, Özgür Özkan, Zhuoran Zhao, Shin-Yeh Tsai, Carole-Jean Wu, Mark Hempstead:
Understanding Capacity-Driven Scale-Out Neural Recommendation Inference. 162-171 - Yasuo Ishii, Jaekyu Lee, Krishnendra Nathella, Dam Sunwoo:
Re-establishing Fetch-Directed Instruction Prefetching: An Industry Perspective. 172-182 - Bobby R. Bruce, Ayaz Akram, Hoa Nguyen, Kyle Roarty, Mahyar Samani, Marjan Fariborz, Trivikram Reddy, Matthew D. Sinclair, Jason Lowe-Power:
Enabling Reproducible and Agile Full-System Simulation. 183-193 - Mark Hildebrand, Julian T. Angeles, Jason Lowe-Power, Venkatesh Akella:
A Case Against Hardware Managed DRAM Caches for NVRAM Based Systems. 194-204 - Mengchi Zhang, Ahmad Alawneh, Timothy G. Rogers:
Characterizing Massively Parallel Polymorphism. 205-216
Poster Session B
- Jiansong Li, Xiao Dong, Guangli Li, Peng Zhao, Xueying Wang, Xiaobing Chen, Xianzhi Yu, Yongxin Yang, Zihan Jiang, Wei Cao, Lei Liu, Xiaobing Feng:
Pinpointing the Memory Behaviors of DNN Training. 217-219 - Guru Prasad Srinivasa, David Werner, Mark Hempstead, Geoffrey Challen:
Thermal-Aware Overclocking for Smartphones. 220-222 - Pablo Bodmann, George Papadimitriou, Dimitris Gizopoulos, Paolo Rech:
The Impact of SoC Integration and OS Deployment on the Reliability of Arm Processors. 223-225 - Jingyi Xu, Sehoon Kim, Borivoje Nikolic, Yakun Sophia Shao:
Memory-Efficient Hardware Performance Counters with Approximate-Counting Algorithms. 226-228 - Francis Wang, Yannan Nellie Wu, Matthew E. Woicik, Joel S. Emer, Vivienne Sze:
Architecture-Level Energy Estimation for Heterogeneous Computing Systems. 229-231 - Yannan Nellie Wu, Po-An Tsai, Angshuman Parashar, Vivienne Sze, Joel S. Emer:
Sparseloop: An Analytical, Energy-Focused Design Space Exploration Methodology for Sparse Tensor Accelerators. 232-234 - Eduardo José Gómez-Hernández, Ruixiang Shao, Christos Sakalis, Stefanos Kaxiras, Alberto Ros:
Splash-4: Improving Scalability with Lock-Free Constructs. 235-236 - Wonkyung Jung, Eojin Lee, Sangpyo Kim, Namhoon Kim, Keewoo Lee, Chohong Min, Jung Hee Cheon, Jung Ho Ahn:
Accelerating Fully Homomorphic Encryption Through Microarchitecture-Aware Analysis and Optimization. 237-239 - Subhankar Pal, Swagath Venkataramani, Viji Srinivasan, Kailash Gopalakrishnan:
Efficient Management of Scratch-Pad Memories in Deep Learning Accelerators. 240-242
Paper Session VI: Datacenters and HPC
- Zahra Azad, Rathijit Sen, Kwanghyun Park, Ajay Joshi:
Hardware Acceleration for DBMS Machine Learning Scoring: Is It Worth the Overheads? 243-253 - Abenezer Wudenhe, Hung-Wei Tseng:
TPUPoint: Automatic Characterization of Hardware-Accelerated Machine-Learning Behavior for Cloud Computing. 254-264 - Takuya Fukuoka, Shigeyuki Sato, Kenjiro Taura:
Pitfalls of InfiniBand with On-Demand Paging. 265-275 - Zhi-Lin Ke, Hsiang-Yun Cheng, Chia-Lin Yang, Han-Wei Huang:
Analyzing the Interplay Between Random Shuffling and Storage Devices for Efficient Machine Learning. 276-287
Paper Session VII: HW and Co-Design
- Sheng-Chun Kao, Tushar Krishna:
E3: A HW/SW Co-design Neuroevolution Platform for Autonomous Learning in Edge Device. 288-298 - Nathan Pemberton, Alon Amid:
FireMarshal: Making HW/SW Co-Design Reproducible and Reliable. 299-309 - Jerry Zhao, Abraham Gonzalez, Alon Amid, Sagar Karandikar, Krste Asanovic:
COBRA: A Framework for Evaluating Compositions of Hardware Branch Predictors. 310-320
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