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ORCIDACM Transactions on Architecture and Code Optimization, Volume 23
Volume 23, Number 1, March 2026
Corrections
- Qiang Liu
, Yihao Hua, Yuhui Hao, Bo Yu, Shaoshan Liu, Yiming Gan:
Corrigendum: Unified and Efficient Factor Graph Accelerator Design for Robotic Optimization. C1:1
Volume 23, Number 2, June 2026
- Minchen Yu, Ao Wang, Bohui Wu, Yuxuan Liu, Dong Chen, Haoxuan Yu, Wei Wang, Ruichuan Chen, Dapeng Nie, Haoran Yang, Yu Ding:
Enabling Low-Latency, GPU-Efficient Serverless Inference with Model Swapping. 39:1-39:27 - Hanjiang Liu, Youcong Ni, Xin Du, Yifu Lu, Dingbang Fang, Yongji Xu:
OSRC-RL: Optimizing Software Reliability at Compile-Time Using Reinforcement Learning. 40:1-40:28 - Fulin Nan, Liang Chen, Zehai Chen, Ronglong Wu, Zhirong Shen, Zhifeng Bao, Dmitrii Kaplun, Jiwu Shu:
FlexRT: Enabling Flexible and Efficient Redundancy Transitioning in Erasure-Coded Systems. 41:1-41:24 - Song Liu, Fei Li, Chenyu Zhao, Qin Xia, Shiqiang Nie, Jinyu Wang, Weiguo Wu:
FDSR: Efficient Model Training via Adaptive Tensor Quantization Based on Frequency Domain Division and Similarity Data Reuse. 42:1-42:26 - Meng Hao, Zikun Wu, Xueyang Tian, Siyu Yang, Guotong Guo, Hongwei Yang, Hui He, Yiming Wang, Farui Wang, Desheng Wang, Weizhe Zhang:
PctoDL: Adaptive GPU Throughput Optimization for Deep Learning Inference with Power Constraints. 43:1-43:25 - Ziheng Wang, Chaoqun Zhang, Yifan Li, Yuchao Wu, Yuping Wang, Xiaoshe Dong:
BLG-Tuning: Benchmark-Based Low-Cost General-Purpose I/O Modeling and Tuning. 44:1-44:24 - Haozhong Qiu, Chuanfu Xu, Qingsong Wang, Jianbin Fang, Jian Zhang, Liang Deng, Yue Ding, Yue Wang, Zhimeng Han, Yonggang Che:
Towards Efficient Symmetric Sparse Matrix-Vector Multiplication on Multi-Cores. 45:1-45:26 - Shihan Yuan, Zuoyan Zhang, Guanghui Song, Junhui Peng, Feng Wang, Zhuo Tang, Kenli Li, Jie Zhao:
A Decoupled Analytical Model for Tile Size Selection in Affine Programs. 46:1-46:25 - Zheming Yang, Wen Ji, Qi Guo, Jian Zhao, Chang Zhao, Xingzhou Zhang, Yangyu Zhang, Zhicheng Li, Yang You:
CLAP: Cross-Layer Adaptive Pipelining Inference Scheduling for Resource-Efficient Edge-Cloud Vision Systems. 47:1-47:25 - Menghan Jia, Yongquan Fu, Yiming Zhang, Xinhai Chen, Dongsheng Li:
FastCC: A System-Algorithm Co-Design for Connected Components Computation on Large Power-Law Graphs. 48:1-48:25 - Qunyou Liu, Marina Zapater, David Atienza:
Mitigating the Bandwidth Wall via Data-Streaming System-Accelerator Co-Design. 49:1-49:26 - Kanheng Jiang, Yongxin Lyu, Zhiyuan Zhang, Zengshi Wang, Chao Fu, Jun Han:
Thoth: Uncovering Data-Dependent Memory Access Patterns via Annotation-Directed Load Sampling. 50:1-50:26 - Yifei Liu, Zuo Gan, Zhenghao Gan, Weiye Wang, Chen Chen, Yizhou Shan, Xusheng Chen, Zhenhua Han, Yifei Zhu, Shixuan Sun, Minyi Guo:
Hermes: Efficient Serving of LLM Applications with Probabilistic Demand Modeling. 51:1-51:25 - Xinxin Qi, Jianbin Fang, Peng Zhang, Yonggang Che:
Optimizing Attention for Large Language Model Inference on the MT-3000 Many-Core Processor. 52:1-52:27 - Kangkang Chen, Huayou Su, Xi Yang, Zitong An, Yong Dou, Dongsheng Li:
Quantitative Analysis and Performance Optimization of Graph Neural Networks on Multi-core CPUs. 53:1-53:25 - Xuanyi Li, Chen Li, Zhengyi Dai, Xiaowen Chen, Jianzhuang Lu, Yang Guo:
WSGraph: A Framework for Tackling Redundant and Irregular Data Access in Streaming Graph Processing. 54:1-54:25 - Deshun Bi, Shengguo Li, Haozhong Qiu, Chuanfu Xu, Xiaojian Yang, Fan Yuan, Dezun Dong, Tiaojie Xiao, Jie Liu:
A Memory-Aware Sparse Matrix-Matrix Multiplication on Multicore Architectures. 55:1-55:21 - Thais Aparecida Silva Camacho, Lucas Fernando Alvarenga e Silva, Márcio Machado Pereira, Guido Araujo:
Using Task Graph Caching to Accelerate TVM Code Generation. 56:1-56:26 - Alexandros Moiras, Konstantinos Iliakis, Dimitrios Soudris, Sotirios Xydis:
AccelHSA: Modeling Single-ISA Heterogeneous GPU Architectures. 57:1-57:25 - Hui Sun, Xiangxiang Jiang, Jinfeng Xu, Enhui Wang, Zheng Fang, Yi Zou, Yinliang Yue, Xiao Qin:
GDH+: A GPGPU-Empowered Gradient Data Hierarchy and Key-Value Separation for Optimizing LSM-Tree-Based KV Stores. 58:1-58:27 - Sichao Chen, Desheng Wang, Weizhe Zhang, Meng Hao, Yu-Chu Tian, Hui He:
Performance Prediction of Concurrent DNN Training Tasks in GPU Spatial Sharing Environments. 59:1-59:25 - Guang Wu, Xinbiao Gan, Songzhu Mei, Zhengbin Pang, Hongxu Jin:
HierMine: Accelerating Graph Pattern Mining via Hierarchical Sampling. 60:1-60:25 - Zhixiang Wei, Zhibai Huang, James Yen, Tianlei Xiong, Kailiang Xu, Yucheng Zheng, Xingzi Yu, Chenggang Wu, Yun Wang, Zhengwei Qi:
SpiderSense: Lightweight Last-Level Cache Management via Time Period Tagging for LLC-Critical Workloads. 61:1-61:24 - Farzana Ahmed Siddique, Deyuan Guo, Hugo Abbot, Kyle Durrer, MohammadHosein Gholamrezaei, Morteza Baradaran, Ethan Ermovick, Alif Ahmed, Zhenxing Fan, Beenish Gul, Ashish Venkat, Kevin Skadron:
Characterizing Digital DRAM PIM through Modeling and Benchmarking. 62:1-62:28 - Zhewen Xu, Hongliang Li, Junze Han, Hengshan Yue, Hairui Zhao, Dongyuan Tian, Zijian Li, Xiaohui Wei:
Sift: Channel-Wise Historical Embedding for High Efficiency Distributed Graph Neural Network Training with Accuracy Guarantee. 63:1-63:26 - Jiuchen Shi, Jinyuan Chen, Quan Chen, Kaihua Fu, Fanrong Du, Zijun Li, Deze Zeng, Jiannong Cao, Shuo Quan, Jie Wu, Minyi Guo:
Delphinus: Improving Resource Efficiency of Applications with Shared Microservices and Diverse Queries. 64:1-64:26 - Guillaume Iooss, Christophe Guillon, Fabrice Rastello, Albert Cohen, P. Sadayappan:
Analytical Modeling of Set-Associative Caches for Optimizing Tensor Operations. 65:1-65:25 - Daon Park, Bernhard Egger:
CPU-GPU Workload Distribution during Throughput-Oriented LLM Inference on Single-GPU Systems. 66:1-66:25 - Zhenlin Qi, Shengan Zheng, Bowen Zhang, Linpeng Huang:
SwitchFS: Exploiting Persistent Memory Bandwidth in Cluster via Learning-based I/O Path Selection. 67:1-67:26 - Wen Li, Zheng Zhang, Jie Zhao, Song Zhu, Ming Hui:
BLR-Krylov: A Single-GPU Iterative SpMM Framework with Communication Avoidance and Block Low-Rank Optimization. 68:1-68:32 - Ruoxi Wang, Dunbo Zhang, Shangshang Yao, Qingjie Lang, Junyi Zhu, Li Shen:
Enabling Efficient Vector Processing: A Heterogeneous Vector Architecture with in-SRAM Computing. 69:1-69:18 - Rongyu Deng, Juan Chen, Zihan Zhang, Yuan Yuan, Yong Dong, Aolin Cao, Zhaoyang Ma, Yida Gu, Dingwen Tao:
CML-PowF: Data Clustering Matching Based Low-overhead Multiple CPU Real-time Power Forecasting. 70:1-70:27 - Junyi Mei, Shixuan Sun, Chao Li, Xinkai Wang, Jing Wang, Xiaofeng Hou, Minyi Guo, Yongchao Liu, Chuntao Hong:
DGS: A GPU-based Adaptive Graph Sampling Framework. 71:1-71:26 - Haolang Yin, Weiwei Lin, Huikang Huang, Xiaoxuan Luo, Haocheng Zhong, Keqin Li:
PaTGen: Temporal Similarity-Driven Proxy Benchmark Generation Method for Cloud Workloads. 72:1-72:25 - Sunil Kumar, Vivek Kumar, Sridutt Bhalachandra:
Power Scheduling for Maximizing Throughput and Fairness in Co-running Applications. 73:1-73:26 - Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Timo Schneider, Saleh Ashkboos, Torsten Hoefler:
STen: Productive and Efficient Sparsity in PyTorch. 74:1-74:25 - Kai Lu, Yuanhui Zhou, Nengjie Wang, Jiguang Wan, Bisheng Huang, Yang Zhang, Jinpeng Zhang, Yu Dong:
HeapKV: Enabling Efficient Garbage Collection for KV-Separated LSM Stores on Modern SSDs. 75:1-75:26 - Zhengwei Huang, Wei Guo, Yongwen Wang:
Apollo: Accelerating Load Requests via Multi-Level Cache Miss Load Prediction. 76:1-76:25 - Yangyu Zhang, Guangli Li, Feng Yu, Fan Luo, Qianqi Sun, Xueying Wang, Jiacheng Zhao, Huimin Cui, Xiaobing Feng, Jingling Xue:
MoonPoly: Bridging Code Generation and Adaptive Execution via Micro-Kernel Polymerization for Optimizing Dynamic-Shape Tensor Operators. 77:1-77:25 - Weichuang Zhang, Yufei Shangguan, Haibo Xu, Yuting Mai, Qiuliang Wang, Chen Chen, Yi Li, Quan Chen, Wenchao Ding, Jieru Zhao, Minyi Guo:
NPUMeter: Automatic Operator Optimization for Ascend NPU with Accurate Analytical Performance Models. 78:1-78:26 - Bingyu Gao, Ziming Wang, Mengyu Yao, Zhihong Xue, Xiangqun Chen, Ding Li, Yao Guo:
PredComp: Predicting Compiler Optimization Options with Multi-stage Learning. 79:1-79:25
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