| layout | title | permalink |
|---|---|---|
page |
/ |
Researcher, Lab Director, & Enterprise CEO
"Software Hardware Co-optimization & System Performance Analytics"
- Researcher / Ph.D. Supervisor – School of Software Technology, Zhejiang University
- Director – SPAIL (System Performance Analytics and Intelligence Lab)
- Ph.D. – Computer Science & Engineering, University of Washington, 1996
Advisor: ACM/IEEE Fellow David Notkin
- Chief Scientist – Alibaba (2016–2022)
- Principal Engineer – Intel Corporation, USA (1996–2016)
- Focus: Software-Hardware Co-optimization (SHCO), Performance Analytics & Intelligence
- Accumulated industry savings: > 💰USD 20 billion
- Scale: Optimized tens of millions of servers worldwide, including Double-11 peak workloads
- Java Standards: First and only Chinese member, JCP-EC (2018–2022)
- Publications: 135+ papers; 74 patents (24 granted US patents)
Leading a team of industry veterans and top researchers to solve bottlenecks in Cloud, AI, and Big Data.
Platform for Integrated Performance Analytics A unified framework designed to describe, analyze, and optimize system performance across heterogeneous architectures.
Dr. Chow has led large-scale, high-impact collaborations with global technology leaders, demonstrating expertise in full-stack system optimization. The projects he has spearheaded accumulated an astonishing total budget exceeding 💰160 million CNY (over 💰20 million USD).
- Strategic Ecosystem Partnerships: Collaborated extensively with industry giants including Amazon, Ampere, Arm, Google, Huawei, Microsoft, Tencent, and Meta.
- Project Apollo (Intel & Oracle, 2014–2016): Led the collaboration for the 2015 Oracle Cloud launch, which was announced by the CEOs of both companies.
- Alibaba SPEED (2018–2020): Led the development of the "System Performance Estimation, Evaluation and Decision" platform for Alibaba.
- Project Meta (Intel & Meta, 2022–2023): A major leadership initiative with a vast budget focused on advanced system research.
- Huawei Software Performance Optimization (2024–2026): Leading a multi-year project dedicated to optimizing Huawei's core software performance.
- Heterogeneous Serverless Optimization (2024–2026): Focused on performance modeling and optimization for serverless, GPU throughput, and microservice environments, collaborating with Alibaba, Kuaishou, ByteDance, and Ampere.
- Alibaba Dragonwell JDK (2018–2019): Spearheaded the development and optimization of Alibaba's critical Java Development Kit.
- Oracle Exalytics Memory Optimization (2013–2014): Led performance optimization for Oracle's in-memory analytics system.
- Intel P6 Microcode Simulator (1993–1994): Early high-impact work involving the development of a performance simulator for Intel's P6 microcode.
I have delivered keynotes at major industry conferences, including 4 appearances at JavaOne, the world's highest-rated Java conference.
-
CMG IMPACT 2022: Propelling Java at Alibaba Scale (Jan 2022)
-
QCon Shanghai 2021: Toward Software Performance Evaluation at Scale: A Journey (Link) (Oct 2021)
-
Arm DevSummit: Keynote Presentation (Nov 2020 & Oct 2020)
-
QCon Beijing: Keynote (2017)
-
JavaOne (San Francisco): Keynote Speaker (2017, 2011, 2008, 2007)
-
JavaOne Keynote (2017)
QCon Shanghai Keynote (2021)
"Kingsum is considered a leading expert across the software and hardware industry for accurate data collection, intuitive analysis and identify optimizations... His knowledge of production systems at scale... resulted in significant performance improvements."
— Anil Rajput, AMD Fellow
"Kingsum's achievements in the realm of software-hardware co-optimization are truly noteworthy. He stands as a globally recognized authority in this domain. His profound understanding and significant international impact... has spurred innovation."
— Prof. Yuan Xie, HKUST (IEEE Fellow, ACM Fellow)
"He has a deep understanding of Intel processors and how to use performance optimization techniques to tune the hardware... He has led have groundbreaking performance improvements."
— Vish Viswanathan, Intel Fellow
"Kingsum is a world-leading expert in this field... widely recognized for his expertise in performance, modeling, and analysis of software applications, with a long history of high-impact work in industry."
— Prof. Ed Lazowska, University of Washington (Member of NAE, AAAS Fellow)
🇺🇸 Granted US Patents（24）
- US10762065 – Performance monitoring
- US10452443 – Dynamic tuning of a multi-processor/core computing system
- US10120731 – Methods and apparatus to measure hardware performance
- US10102134 – Instructions and logic for run-time evaluation of multiple prefetchers
- US10089207 – Performance variation estimation for applications
- US9954744 – Estimating performance variation of an application without prior knowledge
- US9760404 – Dynamic performance optimization for multi-core systems
- US9639884 – Adaptive prefetch throttling
- US9589024 – Performance-aware resource allocation
- US9378021 – Cache management for virtualized environments
- US9286224 – Throttling prefetch requests for a processor socket
- US9223699 – Method and apparatus for energy-efficient prefetching
- US8583507 – Performance counter virtualization
- US8321290 – Business process and apparatus for online buying using rule-based transferable baskets
- US7542924 – Apparatus for dynamic binary translation
- US7454523 – Method for low-overhead performance monitoring
- US7216154 – Apparatus and method for facilitating access to network resources
- US7032017 – System and method for predictive resource allocation
- US6850899 – Method for high-accuracy branch prediction
- US6772324 – Processor having program counter and execution pipeline external trace buffers
- US6741990 – Trace-driven workload characterization
- US6684252 – Method and system for predicting computer-server performance
- US6493820 – System for online performance diagnostics
- US6182210 – Method and apparatus for real-time performance tuning
🇺🇸 Published US Applications（22）
- US20210056086 – Cross-architecture performance projection
- US20170337083 – Cloud-scale performance regression detection
- US20170169064 – Adaptive sampling for large-scale systems
- US20170060635 – Method for updating software with zero downtime
- US20170063652 – Hardware-assisted performance tracing
- US20160299847 – Energy-aware workload scheduling
- US20150378861 – Performance anomaly detection using ML
- US20150234663 – Cache partitioning for multi-tenant systems
- US20150220372 – Method for fast micro-benchmark synthesis
- US20150220528 – Scalable performance counters
- US20150149714 – Dynamic voltage/frequency control
- US20140281230 – Cross-platform binary instrumentation
- US20140222617 – Hardware-support for managed-runtime profiling
- US20130103541 – Predictive power management
- US20090307108 – Method for scalable event tracing
- US20050131772 – System for automated bottleneck analysis
- US20030097412 – Method for high-resolution time measurement
- US20030061360 – Framework for continuous performance validation
- US20030033511 – Adaptive feedback-driven optimization
- US20020178169 – System for heterogeneous workload co-location
- US20020143991 – Method for lightweight memory profiling
- US20010014941 – Early-stage performance modeling
🇨🇳 中国专利（已公开/授权）
- CN111435317B – 数据处理方法、计算设备及存储介质（发明人：郭健美、周经森；权利人：阿里巴巴集团；已授权）
- CN110998539B – 系统更新的性能影响分析（发明人：周经森、朱婉怡；权利人：阿里巴巴集团；已授权）
- CN110235085A – 确定多处理系统的处理器使用率（发明人：周经森等；权利人：阿里巴巴集团）
- CN110741351A – 确定虚拟化多处理系统的处理器利用率
- CN105164651A – 在管理的运行时间环境域中的高速缓存管理（权利人：英特尔）
- CN111435317A – 数据处理方法、计算设备及存储介质（公开）
- CN107851041A – 多处理器/多核心计算系统的动态调优（权利人：英特尔）
- CN110741351B – 确定虚拟化多处理系统的处理器利用率（授权）
- CN107851041B – 多处理器/多核心计算系统的动态调优（授权）
- CN110998539A – 系统更新的性能影响分析（公开）
- CN105164651B – 在管理的运行时间环境域中的高速缓存管理（授权）
🇨🇳 中国专利申请（已受理 / 实审中）
- 一种面向混合架构的CPU利用率的计算系统和方法. 发明人：周经森、江新宇、冯雨森、管江涛. 状态：实审中. 申请日：2023.11
- 一种基于机器学习的数据库性能预测方法. 发明人：周经森、孙志超. 状态：实审中. 申请日：2024.11.20
- 一种面向电商秒杀应用的基准测试方法. 发明人：周经森、陈奕坤、杨孟铎、常亚辰、江新宇、章超. 状态：将要授权. 申请日：2024.10.31；预计授权日：2025.10.20
- 一种基于类别感知和特征解耦的分布外检测方法. 发明人：周经森、常亚辰、凌志威、赵海亮. 状态：实审中. 申请日：2025.01.23
- 一种云服务器异常检测方法. 发明人：周经森、梁冬晴. 状态：实审中. 申请日：2025.01.22
- 一种自动提取并行应用程序热点代码的方法. 发明人：周经森、章超. 状态：将要授权. 申请日：2024.12.09；预计授权日：2025.09.26
- 一种多个核心组内共享预取器的预取配置优化方法. 发明人：周经森、常亚辰. 状态：将要授权. 申请日：2024.12.10；预计授权日：2025.09.25
- 一种面向数据中心集群的多重连接聚类方法. 发明人：周经森、冯雨森. 状态：将要授权. 申请日：2024.12.06；预计授权日：2025.09.22
- 一种CPU性能采样工具的运行开销的预测方法. 发明人：周经森、汤煜. 状态：受理. 申请日：2025.07.08
- 一种基于分布外检测的联邦学习方法. 发明人：周经森、章超、赵海亮、凌志威. 状态：受理. 申请日：2025.04.01
- 一种计算机处理器性能监测单元的硬件事件组调度方法. 发明人：周经森、江新宇. 状态：受理. 申请日：2025.07.08
- 一种基于LLM聚类和多次召回的文档检索方法. 发明人：周经森、管江涛. 状态：受理. 申请日：2025.10.11
🌐 International Applications（5）
- LinkedIn: Kingsum Chow
- Email: ksumchow@outlook.com
- Location: Ningbo, Zhejiang, China