English
Related papers

Related papers: CXL-GPU: Pushing GPU Memory Boundaries with the In…

200 papers

Memory disaggregation via CXL enables multi-host resource sharing. However, existing CXL sharing mechanisms enforce coarse-grained, host-level permissions only, leaving isolation to the operating system. Today, virtual memory enables…

Hardware Architecture · Computer Science 2026-05-29 Kaustav Goswami , Sean Peisert , Venkatesh Akella , Jason Lowe-Power

Engram conditional memory has emerged as a promising component for LLMs by decoupling static knowledge lookup from dynamic computation. Since Engram exhibits sparse access patterns and supports prefetching, its massive embedding tables are…

Hardware Architecture · Computer Science 2026-03-12 Ruiyang Ma , Teng Ma , Zhiyuan Su , Hantian Zha , Xinpeng Zhao , Xuchun Shang , Xingrui Yi , Zheng Liu , Zhu Cao , An Wu , Zhichong Dou , Ziqian Liu , Daikang Kuang , Guojie Luo

Transaction processing systems are the crux for modern data-center applications, yet current multi-node systems are slow due to network overheads. This paper advocates for Compute Express Link (CXL) as a network alternative, which enables…

Hardware Architecture · Computer Science 2025-07-24 Zhao Wang , Yiqi Chen , Cong Li , Dimin Niu , Tianchan Guan , Zhaoyang Du , Xingda Wei , Guangyu Sun

Heterogeneous memory technologies are increasingly important instruments in addressing the memory wall in HPC systems. While most are deployed in single node setups, CXL.mem is a technology that implements memories that can be attached to…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-12-10 Stepan Vanecek , Matthew Turner , Manisha Gajbe , Matthew Wolf , Martin Schulz

While Compute Express Link (CXL) enables support for cache-coherent shared memory among multiple nodes, it also introduces new types of failures--processes can fail before data does, or data might fail before a process does. The lack of a…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-07-18 Yi Xu , Suyash Mahar , Ziheng Liu , Mingyao Shen , Steven Swanson

The ability to scale out training workloads has been one of the key performance enablers of deep learning. The main scaling approach is data-parallel GPU-based training, which has been boosted by hardware and software support for highly…

Distributed, Parallel, and Cluster Computing · Computer Science 2023-01-02 Ilia Markov , Hamidreza Ramezanikebrya , Dan Alistarh

The emergence of CXL (Compute Express Link) promises to transform the status of interconnects between host and devices and in turn impact the design of all software layers. With its low overhead, low latency, and memory coherency…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-04-15 Raja Gond , Purushottam Kulkarni

The substantial memory requirements of Large Language Models (LLMs), particularly for long-context fine-tuning, have renewed interest in CPU offloading to augment limited GPU memory. However, as context lengths grow, relying on CPU memory…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-10-31 Yong-Cheng Liaw , Shuo-Han Chen

Compute Express Link (CXL) 3.0 and beyond allows the compute nodes of a cluster to share data with hardware cache coherence and at the granularity of a cache line. This enables shared-memory semantics for distributed computing, but…

Distributed, Parallel, and Cluster Computing · Computer Science 2026-02-10 Antonis Psistakis , Burak Ocalan , Chloe Alverti , Fabien Chaix , Ramnatthan Alagappan , Josep Torrellas

We present a thorough analysis of the use of modern heterogeneous systems interconnected by various cachecoherent links, including CXL, NVLink-C2C, and Infinity Fabric. We studied a wide range of server systems that combined CPUs from…

The expansion of context windows in large language models (LLMs) to multi-million tokens introduces severe memory and compute bottlenecks, particularly in managing the growing Key-Value (KV) cache. While Compute Express Link (CXL) enables…

Tremendous advances in parallel computing and graphics hardware opened up several novel real-time GPU applications in the fields of computer vision, computer graphics as well as augmented reality (AR) and virtual reality (VR). Although…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-08-19 Patrick Stotko

Massively multicore processors, such as Graphics Processing Units (GPUs), provide, at a comparable price, a one order of magnitude higher peak performance than traditional CPUs. This drop in the cost of computation, as any…

Distributed, Parallel, and Cluster Computing · Computer Science 2016-11-18 Samer Al-Kiswany , Abdullah Gharaibeh , Matei Ripeanu

Increasing workload demands and emerging technologies necessitate the use of various memory and storage tiers in computing systems. This paper presents results from a CXL-based Experimental Memory Request Logger that reveals precise memory…

Operating Systems · Computer Science 2025-08-14 Vinicius Petrucci , Felippe Zacarias , David Roberts

Hardware based memory pooling enabled by interconnect standards like CXL have been gaining popularity amongst cloud providers and system integrators. While pooling memory resources has cost benefits, it comes at a penalty of increased…

Hardware Architecture · Computer Science 2024-06-24 Chandrahas Tirumalasetty , Narasimha Annapreddy

The vision of super computer at every desk can be realized by powerful and highly parallel CPUs or GPUs or APUs. Graphics processors once specialized for the graphics applications only, are now used for the highly computational intensive…

Distributed, Parallel, and Cluster Computing · Computer Science 2012-04-16 Chittampally Vasanth Raja , Srinivas Balasubramanian , Prakash S Raghavendra

Despite the high computational throughput of GPUs, limited memory capacity and bandwidth-limited CPU-GPU communication via PCIe links remain significant bottlenecks for accelerating large-scale data analytics workloads. This paper…

Databases · Computer Science 2025-02-14 Yichao Yuan , Advait Iyer , Lin Ma , Nishil Talati

As the memory channel count is confined by physical dimensions, memory expanders appear to be a promising approach to extending memory capacity and channels by augmenting the existing I/O interface (e.g., PCIe) with memory-semantic…

Hardware Architecture · Computer Science 2026-03-30 Younghoon Ko , Hyemin Park , Hyuk-Jae Lee , Hyokeun Lee

CXL-based Computational Memory (CCM) enables near-memory processing within expanded remote memory, presenting opportunities to address data movement costs associated with disaggregated memory systems and to accelerate overall performance.…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-12-05 Suyeon Lee , Kangkyu Park , Kwangsik Shin , Ada Gavrilovska

Compute Express Link (CXL) serves as a rising industry standard, delivering high-speed cache-coherent links to a variety of devices, including host CPUs, computational accelerators, and memory devices. It is designed to promote system…

Hardware Architecture · Computer Science 2024-11-14 Yuda An , Shushu Yi , Bo Mao , Qiao Li , Mingzhe Zhang , Ke Zhou , Nong Xiao , Guangyu Sun , Xiaolin Wang , Yingwei Luo , Jie Zhang