Changyeon Jo

selected publications

1. MICRO’26

   MangoBoost FORMULA: A Fast, Scalable and Flexible AI RNIC

   Kanghyun Choi, Bogyeong Park, Gihwan Lee, Hanmin Kim, Nayeon Kim, Chanmyeong Kim, Hyeonseong Choi, Dongjoo Lee, Jaehyun Lee, Eunjin Baek, Changsu Kim,  Changyeon Jo, Minho Chu, and Jangwoo Kim

   In 59th IEEE/ACM International Symposium on Microarchitecture (MICRO-59), Industry Track 2026
2. IEEE Micro

   MangoBoost Alice: Extremely Fast, Seamless, and Versatile FPGA-Accelerated DPU Solutions

   Heetaek Jeong, Wonsik Lee, Eunjin Baek, Changsu Kim,  Changyeon Jo, Dongju Chae, Kanghyun Choi, Hamin Jang, Mohamed A. Elgammal, Sungmin Hong, Eriko Nurvitadhi, Dongup Kwon, and Jangwoo Kim

   IEEE Micro Sep 2025
3. PPoPP’22

   Dopia: Online Parallelism Management for Integrated CPU/GPU Architectures

   Younghyun Cho, Jiyeon Park, Florian Negele,  Changyeon Jo, Thomas R. Gross, and Bernhard Egger

   In 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP 2022) Apr 2022

   Abs PDF

   Recent desktop and mobile processors often integrate CPU and GPU onto the same die. The limited memory bandwidth of these integrated architectures can negatively affect the performance of data-parallel workloads when all computational resources are active. The combination of active CPU and GPU cores achieving the maximum performance depends on a workload’s characteristics, making manual tuning a time-consuming task. Dopia is a fully automated framework that improves the performance of data-parallel workloads by adjusting the Degree Of Parallelism on Integrated Architectures. Dopia transparently analyzes and rewrites OpenCL kernels before executing them with the number of CPU and GPU cores expected to yield the best performance. Evaluated on AMD and Intel integrated processors, Dopia achieves 84% of the maximum performance attainable by an oracle.

4. PACT’20

   RackMem: A Tailored Caching Layer for Rack Scale Computing

   Changyeon Jo, Hyunik Kim, Hexiang Geng, and Bernhard Egger

   In Proceedings of the 2020 International Conference on Parallel Architectures and Compilation (PACT’20) Apr 2020

   Abs PDF

   High-performance computing (HPC) clusters suffer from an overall low memory utilization that is caused by the node-centric memory allocation combined with the variable memory requirements of HPC workloads. The recent provisioning of nodes with terabytes of memory to accommodate workloads with extreme peak memory requirements further exacerbates the problem. Memory disaggregation is viewed as a promising remedy to increase overall resource utilization and enable cost-effective up-scaling and efficient operation of HPC clusters, however, the overhead of demand paging in virtual memory management has so far hindered performant implementations. To overcome these limitations, this work presents RackMem, an efficient implementation of disaggregated memory for rack scale computing. RackMem addresses the shortcomings of Linux’s demand paging algorithm and automatically adapts to the memory access patterns of individual processes to minimize the inherent overhead of remote memory accesses. Evaluated on a cluster with an Infiniband interconnect, RackMem outperforms the state-of-the-art RDMA implementation and Linux’s virtual memory paging by a significant margin. RackMem’s custom demand paging implementation achieves a tail latency that is two orders of magnitude better than that of the Linux kernel. Compared to the state-of-the-art remote paging solution, RackMem achieves a 28% higher throughput and a 44% lower tail latency for a wide variety of real-world workloads.

5. SoCC’17

   100+ citations

   A Machine Learning Approach to Live Migration Modeling

   Changyeon Jo, Youngsu Cho, and Bernhard Egger

   In ACM Symposium on Cloud Computing (SoCC’17) Sep 2017

   Abs PDF Code

   Live migration is one of the key technologies to improve data center utilization, power efficiency, and maintenance. Various live migration algorithms have been proposed; each exhibiting distinct characteristics in terms of completion time, amount of data transferred, virtual machine (VM) downtime, and VM performance degradation. To make matters worse, not only the migration algorithm but also the applications running inside the migrated VM affect the different performance metrics. With service-level agreements and operational constraints in place, choosing the optimal live migration technique has so far been an open question. In this work, we propose an adaptive machine learning-based model that is able to predict with high accuracy the key characteristics of live migration in dependence of the migration algorithm and the workload running inside the VM. We discuss the important input parameters for accurately modeling the target metrics, and describe how to profile them with little overhead. Compared to existing work, we are not only able to model all commonly used migration algorithms but also predict important metrics that have not been considered so far such as the performance degradation of the VM. In a comparison with the state-of-the-art, we show that the proposed model outperforms existing work by a factor 2 to 5.

6. VEE’13

   100+ citations

   Efficient live migration of virtual machines using shared storage

   Changyeon Jo, Erik Gustafsson, Jeongseok Son, and Bernhard Egger

   In Proceedings of the 9th ACM SIGPLAN/SIGOPS international conference on Virtual Execution Environments (VEE’13) Sep 2013

   Abs PDF

   Live migration of virtual machines (VM) across distinct physical hosts is an important feature of virtualization technology for maintenance, load-balancing and energy reduction, especially so for data centers operators and cluster service providers. Several techniques have been proposed to reduce the downtime of the VM being transferred, often at the expense of the total migration time. In this work, we present a technique to reduce the total time required to migrate a running VM from one host to another while keeping the downtime to a minimum. Based on the observation that modern operating systems use the better part of the physical memory to cache data from secondary storage, our technique tracks the VM’s I/O operations to the network-attached storage device and maintains an updated mapping of memory pages that currently reside in identical form on the storage device. During the iterative pre-copy live migration process, instead of transferring those pages from the source to the target host, the memory-to-disk mapping is sent to the target host which then fetches the contents directly from the network-attached storage device. We have implemented our approach into the Xen hypervisor and ran a series of experiments with Linux HVM guests. On average, the presented technique shows a reduction of up over 30% on average of the total transfer time for a series of benchmarks.