publications

2026

1. Cxlalloc: Safe and Efficient Memory Allocation for a CXL Pod

   Newton Ni, Yan Sun, Zhiting Zhu, and 1 more author

   In Proceedings of the 31st ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2, USA, 2026

   Abs DOI PDF Code

   A Compute Express Link (CXL) pod is a group of hosts that share CXL-attached memory. A memory allocator for a CXL pod faces novel challenges: (1) CXL devices may not fully support inter-host hardware cache coherence (HWcc), (2) the allocator may be concurrently accessed from different processes, and (3) with more hosts, failures become more likely.We present cxlalloc, a user-space memory allocator that addresses these challenges through careful metadata layout and new protocols to maintain cache coherence in software, coordinate memory mappings across processes, and recover from crashes. Cxlalloc uses compare-and-swap (CAS) for efficient synchronization; to support CXL devices with no HWcc, we present a memory-based CAS (mCAS) primitive implemented in an FPGA.Experiments with in-memory key-value store workloads demonstrate that cxlalloc retains competitive performance while enabling new use-cases. Experiments with a commercial CXL device show that cxlalloc can achieve 80% of its maximum allocation throughput using mCAS.

2025

1. Impeller: Stream Processing on Shared Logs

   Zhiting Zhu, Zhipeng Jia, Newton Ni, and 2 more authors

   In Proceedings of the Twentieth European Conference on Computer Systems, Rotterdam, Netherlands, 2025

   Abs DOI PDF Code

   Current stream processing systems provide exactly-once semantics using checkpointing or a combination of logging and checkpointing. These approaches can introduce high overhead, significantly increasing the latency for normal stream processing because maintaining exactly-once semantics requires coordination across distributed nodes and streams to capture a globally consistent state. We observe that modern distributed shared logs offer a promising solution for maintaining exactly-once semantics with a small overhead. We propose Impeller, a stream processing system that uses a distributed shared log for data storage and exactly-once processing. To maintain exactly-once semantics, Impeller includes a novel and efficient progress marking protocol based on string tags and selective reads in a shared log. The key idea is to leverage the log’s record-tagging feature to atomically mark progress across all streams. The experiments over the NEXMark benchmark show that Impeller achieves 1.3\texttimes to 5.4\texttimes lower p50 latency, or 1.3\texttimes to 5.0\texttimes higher saturation throughput than Kafka Streams.

2024

1. Lupin: Tolerating Partial Failures in a CXL Pod

   Zhiting Zhu, Newton Ni, Yibo Huang, and 4 more authors

   In Proceedings of the 2nd Workshop on Disruptive Memory Systems, Austin, TX, USA, 2024

   Abs DOI PDF Code

   A compute express link (CXL) pod is a collection of hosts attached to a CXL memory module. It provides an opportunity to port single-host shared-memory programs to execute on multiple hosts in a CXL pod, where the ported application achieves higher performance than a distributed application that uses network for coordination. The cost of performance scaling on a CXL pod is that applications should tolerate partial failures, where one process or operating system fails or reboots. Lupin is system software that includes kernel modifications and user-level libraries to help applications remain available while they recover from partial failures using the contents of CXL memory.

2023

1. Disaggregated GPU Acceleration for Serverless Applications

   Henrique Fingler, Zhiting Zhu, Esther Yoon, and 3 more authors

   SIGOPS Oper. Syst. Rev., Jun 2023

   Abs DOI

   Serverless platforms have been attracting applications from traditional platforms because infrastructure management responsibilities are shifted from users to providers. Many applications well-suited to serverless environments could leverage GPU acceleration to enhance their performance. Unfortunately, current serverless platforms do not expose GPUs to serverless applications.

2022

1. DGSF: Disaggregated GPUs for Serverless Functions

   Henrique Fingler, Zhiting Zhu, Esther Yoon, and 3 more authors

   In 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2022

   Abs DOI

   Ease of use and transparent access to elastic resources have attracted many applications away from traditional platforms toward serverless functions. Many of these applications, such as machine learning, could benefit significantly from GPU acceleration. Unfortunately, GPUs remain inaccessible from serverless functions in modern production settings. We present DGSF, a platform that transparently enables serverless functions to use GPUs through general purpose APIs such as CUDA. DGSF solves provisioning and utilization challenges with disaggregation, serving the needs of a potentially large number of functions through virtual GPUs backed by a small pool of physical GPUs on dedicated servers. Disaggregation allows the provider to decouple GPU provisioning from other resources, and enables significant benefits through consolidation. We describe how DGSF solves GPU disaggregation challenges including supporting API transparency, hiding the latency of communication with remote GPUs, and load-balancing access to heavily shared GPUs. Evaluation of our prototype on six workloads shows that DGSF’s API remoting optimizations can improve the runtime of a function by up to 50% relative to unoptimized DGSF. Such optimizations, which aggressively remove GPU runtime and object management latency from the critical path, can enable functions running over DGSF to have a lower end-to-end time than when running on a GPU natively. By enabling GPU sharing, DGSF can reduce function queueing latency by up to 53%. We use DGSF to augment AWS Lambda with GPU support, showing similar benefits.

2019

1. TxFS: Leveraging File-system Crash Consistency to Provide ACID Transactions

   Yige Hu, Zhiting Zhu, Ian Neal, and 4 more authors

   ACM Trans. Storage, May 2019

   Abs DOI

   We introduce TxFS, a transactional file system that builds upon a file system’s atomic-update mechanism such as journaling. Though prior work has explored a number of transactional file systems, TxFS has a unique set of properties: a simple API, portability across different hardware, high performance, low complexity (by building on the file-system journal), and full ACID transactions. We port SQLite, OpenLDAP, and Git to use TxFS and experimentally show that TxFS provides strong crash consistency while providing equal or better performance.

2018

1. TxFS: Leveraging File-System Crash Consistency to Provide ACID Transactions

   Yige Hu, Zhiting Zhu, Ian Neal, and 4 more authors

   In 2018 USENIX Annual Technical Conference (USENIX ATC 18), Jul 2018

   Abs PDF

   We introduce TxFS, a novel transactional file system that builds upon a file system’s atomic-update mechanism such as journaling. Though prior work has explored a number of transactional file systems, TxFS has a unique set of properties: a simple API, portability across different hardware, high performance, low complexity (by building on the journal), and full ACID transactions. We port SQLite and Git to use TxFS, and experimentally show that TxFS provides strong crash consistency while providing equal or better performance.

2. Ryoan: A Distributed Sandbox for Untrusted Computation on Secret Data

   Tyler Hunt, Zhiting Zhu, Yuanzhong Xu, and 2 more authors

   ACM Trans. Comput. Syst., Dec 2018

   Abs DOI Code

   Users of modern data-processing services such as tax preparation or genomic screening are forced to trust them with data that the users wish to keep secret. Ryoan1 protects secret data while it is processed by services that the data owner does not trust. Accomplishing this goal in a distributed setting is difficult, because the user has no control over the service providers or the computational platform. Confining code to prevent it from leaking secrets is notoriously difficult, but Ryoan benefits from new hardware and a request-oriented data model.Ryoan provides a distributed sandbox, leveraging hardware enclaves (e.g., Intel’s software guard extensions (SGX) [40]) to protect sandbox instances from potentially malicious computing platforms. The protected sandbox instances confine untrusted data-processing modules to prevent leakage of the user’s input data. Ryoan is designed for a request-oriented data model, where confined modules only process input once and do not persist state about the input. We present the design and prototype implementation of Ryoan and evaluate it on a series of challenging problems including email filtering, health analysis, image processing and machine translation.

2017

1. Understanding The Security of Discrete GPUs

   Zhiting Zhu, Sangman Kim, Yuri Rozhanski, and 3 more authors

   In Proceedings of the General Purpose GPUs, Austin, TX, USA, 2017

   Abs DOI

   GPUs have become an integral part of modern systems, but their implications for system security are not yet clear. This paper demonstrates both that discrete GPUs cannot be used as secure co-processors and that GPUs provide a stealthy platform for malware. First, we examine a recent proposal to use discrete GPUs as secure co-processors and show that the security guarantees of the proposed system do not hold on the GPUs we investigate. Second, we demonstrate that (under certain circumstances) it is possible to bypass IOMMU protections and create stealthy, long-lived GPU-based malware. We demonstrate a novel attack that compromises the in-kernel GPU driver and one that compromises GPU microcode to gain full access to CPU physical memory. In general, we find that the highly sophisticated, but poorly documented GPU hardware architecture, hidden behind obscure close-source device drivers and vendor-specific APIs, not only make GPUs a poor choice for applications requiring strong security, but also make GPUs into a security threat.

2016

1. Ryoan: A Distributed Sandbox for Untrusted Computation on Secret Data

   Tyler Hunt, Zhiting Zhu, Yuanzhong Xu, and 2 more authors

   In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Nov 2016

   PDF Code