
Efficient system for aligning large language models, optimizing model orchestration and workload switching to minimize overhead and improve performance in AI alignment tasks.

Innovative system for efficient probabilistic graphical model inference, offering significant speedup through optimized memory management, parallelization, and flexible integration with sampling algorithms.

Innovative encryption technique for Docker image deduplication, enhancing storage efficiency and pull performance while preserving privacy in registries.

Adaptive page profiling and migration system for tiered memory, improving performance through dynamic decision-making and combining profiling methods for enhanced memory management.

Innovative transport protocol ETC uses pulling rate as congestion signal, outperforming existing protocols in throughput, latency, convergence speed, and fairness for improved network performance.

Innovative approach to page-table management in NUMA systems, enhancing performance through on-demand replication and reduced TLB shootdowns, benefiting highly threaded applications.

Optimizing deep learning programs by generating complete operator graphs through runtime monitoring, enhancing performance for complex Python-based DNN implementations.

Explore FetchBPF, a framework for customizing Linux memory prefetching policies using eBPF, enabling performance optimization without kernel modifications.

Innovative serverless platform REWIND enhances security and efficiency by resetting containers, using kernel-level snapshots, and optimizing memory usage, improving performance while maintaining data isolation between function requests.

Efficient trapless kernel probing mechanism for Linux, improving performance 10x over standard Kprobe while covering 96% of kernel code. Enhances debugging, security, and monitoring capabilities.

Innovative control plane design using SmartNICs to optimize distributed ML workloads, reducing bottlenecks and improving resource utilization for enhanced performance and efficiency.

Explore cycle-accurate simulation of large-scale chiplet networks using CNSim, a novel packet-parallel simulator. Gain insights into heterogeneous network design and evaluation for advanced chiplet architectures.

Optimize ML data preprocessing costs with Pecan's dynamic worker scheduling and transformation reordering, reducing preprocessing expenses by 87% and total training costs by up to 60%.

Comprehensive analysis of Sunway TaihuLight supercomputer data reveals insights into HPC operational management and I/O workload characteristics, offering valuable lessons for system optimization.

Innovative strategy for optimizing video feed streaming using knowledge graphs to predict content popularity and improve resource allocation, resulting in reduced bandwidth costs and faster download speeds.

StreamCache: A new page cache system for fast storage devices, improving I/O performance through stream tracking, efficient reclaiming, and accelerated page allocation. Enhances scientific apps and large language model operations.

Innovative RDMA framework for multi-tenant cloud environments, offering performance isolation and work conservation without compromising bare-metal performance through a novel RNIC preemption mechanism.

Kivi: A pioneering system for verifying Kubernetes controllers and configurations, addressing challenges in cloud infrastructure management through model checking and optimization techniques.

Empirical study on Rust-for-Linux project, analyzing its impact on kernel safety and performance. Explores successes, challenges, and compromises in integrating Rust into the Linux ecosystem.

Optimizing serverless computing resource utilization with Jiagu: novel techniques for accurate prediction, efficient scaling, and improved deployment density while maintaining QoS.

Explore FiTx, a framework for fast, daily bug detection in Linux. Learn how it balances analysis time and bug-finding capabilities, outperforming existing tools in speed and effectiveness.