Seminars
Simple Techniques for Loop-friendly Eviction and Belady Anomaly Reduction
Many workloads, such as repeated scans and LSM-tree compaction, access groups of objects in similar orders, forming access loops that several cache eviction algorithms handle well—though it remains unclear what makes them effective. Yunjia will distill the structural features behind this efficiency into portable eviction gadgets that can be plugged into existing policies, and show that these features also improve cache stability by reducing miss-ratio anomalies and cliffs.
Diagnosing Performance Issues by Uncovering Application Resource Bottlenecks
Performance issues in large software systems are often driven by application-defined resources like buffer pools and caches, which remain hidden from system-level metrics and are notoriously difficult to diagnose with standard profilers. Youliang will introduce gigiprofiler, a tool that combines LLM-based semantic inference with static analysis to uncover these bottlenecks, and share practical insights on leveraging large language models for program analysis.
A Year in LLM Serving: Workload Evolution, Caching and Load-Balancing
Designing effective modern LLM serving systems requires an understanding of realistic workloads, but capturing the complexity of today’s diverse applications is difficult using only short traces or synthetic datasets. William will share insights from a comprehensive one-year production trace of billions of LLM requests, exploring how these workloads evolve and detailing key systems implications for prefix caching and load balancing.
Sketches and Their Applications for Synchronization, Blockchain Networks and Learning
Hash-based data structures such as Bloom filters are widely used in network systems for a wide range of tasks. In this talk, Ori will overview several recent designs that expand and enhance their utility across multiple domains such as data synchronization among peers, blockchain networks and machine learning pipelines. The talk is based on recent papers from Sigmetrics, TNSM, AFT and APNET.
Systems Seminar: Round Table Discussion
A new addition to our Systems Seminar: an open round table discussion where people are encouraged to share recent papers, tech news, new tools, or open questions in a relaxed, conversational setting.
Building Scalable Distributed Databases in the Age of Geo-Replication
Modern distributed applications depend heavily on geo-replication for fault tolerance, but high network latency forces these databases to make difficult tradeoffs between the high performance of weak consistency and the data safety of strong consistency. Yunhao will explore solutions to these challenges by detailing advancements in Conflict-free Replicated Datatypes (CRDTs), including the Janus implementation, and introducing Minerva, a scalable transaction protocol designed to maintain high throughput across wide-area networks.
Systems at the Crossroad of Agents & Infrastructure (MLSys ’26 Digest Talk)
As Large Language Models transition into autonomous agentic systems, traditional serving frameworks are facing unprecedented performance bottlenecks. Yiyu will deliver a structured digest of cutting-edge research from MLSys 2026 covering agentic AI, LLM systems, and compilers.
Inference and AI Infrastructure (Special Event for #BosTechWeek)
The market focus has shifted from building LLMs and training to how to serve these models and efficient inference. In this meet up we are discussing the AI inference stack and how optimizing it is a multi dimensional problem, the role of hardware and software co-design and how applications are the only deliverable. We also deep dive into MLCommons Chakra, a framework for capturing AI application execution graphs.
Firefly: Scalable, Ultra-Accurate Clock Synchronization for Datacenters
Achieving the sub-10ns clock synchronization required by cloud-based financial exchanges is increasingly difficult because existing methods are often vulnerable to jitter, drift, and the complexities of large-scale network paths. Yuliang will showcase Firefly, a software-driven system that leverages a distributed consensus algorithm and a novel layered synchronization technique to provide resilient, high-precision time alignment across modern datacenters.
High-Dimensional Gradient-Free Optimization for Neuroscience, Interpretability and LLMs: Why It Works and How to Make It Better
Evolution strategies (ES) provide a vital gradient-free alternative for solving complex, high-dimensional optimization problems in fields like neuroscience and LLM fine-tuning where traditional backpropagation is often unavailable or inefficient. Binxu Wang will explore the geometric properties that enable these methods to succeed and demonstrate how identifying task-irrelevant parameter directions can be leveraged to further accelerate optimization in modern large-scale models.