{"ID":2841411,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12349","arxiv_id":"2511.12349","title":"Pushing the Memory Bandwidth Wall with CXL-enabled Idle I/O Bandwidth Harvesting","abstract":"The continual increase of cores on server-grade CPUs raises demands on memory systems, which are constrained by limited off-chip pin and data transfer rate scalability. As a result, high-end processors typically feature lower memory bandwidth per core, at the detriment of memory-intensive workloads. We propose alleviating this challenge by improving the utility of the CPU's limited pins. In a typical CPU design process, the available pins are apportioned between memory and I/O traffic, each accounting for about half of the total off-chip bandwidth availability. Consequently, unless both memory and I/O are simultaneously highly utilized, such fragmentation leads to underutilization of the valuable off-chip bandwidth resources. An ideal architecture would offer I/O and memory bandwidth fungibility, allowing use of the aggregate off-chip bandwidth in the form required by each workload. In this work, we introduce SURGE, a software-supported architectural technique that boosts memory bandwidth availability by salvaging idle I/O bandwidth resources. SURGE leverages the capability of versatile interconnect technologies like CXL to dynamically multiplex memory and I/O traffic over the same processor interface. We demonstrate that SURGE-enhanced architectures can accelerate memory-intensive workloads on bandwidth-constrained servers by up to 1.3x.","short_abstract":"The continual increase of cores on server-grade CPUs raises demands on memory systems, which are constrained by limited off-chip pin and data transfer rate scalability. As a result, high-end processors typically feature lower memory bandwidth per core, at the detriment of memory-intensive workloads. We propose alleviat...","url_abs":"https://arxiv.org/abs/2511.12349","url_pdf":"https://arxiv.org/pdf/2511.12349v1","authors":"[\"Divya Kiran Kadiyala\",\"Alexandros Daglis\"]","published":"2025-11-15T20:32:16Z","proceeding":"cs.AR","tasks":"[\"cs.AR\"]","methods":"[]","has_code":false}