[Paper] Rethinking Inter-Process Communication with Memory Operation Offloading

Source: arXiv - 2601.06331v1

Overview

Modern AI‑driven services move massive amounts of data between processes, and the CPU time spent copying that data can dominate request latency and cost. The paper “Rethinking Inter‑Process Communication with Memory Operation Offloading” proposes a unified runtime that turns memory‑copy‑heavy IPC into a system‑wide capability, leveraging both hardware and software offload mechanisms to cut CPU work, boost throughput, and shrink tail latency.

Key Contributions

• Unified IPC runtime that orchestrates hardware‑ and software‑based memory offloading for shared‑memory communication.
• Characterization framework that maps offload strategies to IPC semantics (synchronization, cache visibility, concurrency).
• Multiple IPC modes (asynchronous pipelined, selective cache injection, hybrid coordination) letting developers trade off latency, throughput, and CPU usage.
• Hybrid offload coordination that abstracts device‑specific features (e.g., DMA engines, NIC‑offloaded RDMA) into a generic system service.
• Empirical evaluation on real‑world AI/ML workloads showing up to 22 % fewer CPU instructions, 2.1× higher throughput, and 72 % lower tail latency.

Methodology

1. System Model & Baseline – The authors start from a conventional POSIX shared‑memory IPC stack (shm_open, mmap, futex) and measure the CPU cycles spent on memory copies and synchronization.
2. Offload Primitives – They expose two primitive operations:
   • Hardware offload (DMA, RDMA, NIC‑based zero‑copy) that moves pages without CPU intervention.
   • Software offload (kernel‑mediated page‑pinning, copy‑elision, lazy cache flush) that reduces the amount of data actually copied.
3. Runtime Scheduler – A lightweight daemon registers the available offload engines, tracks buffer ownership, and decides—per message—whether to use pure software, pure hardware, or a hybrid path based on size, contention, and QoS hints.
4. Mode Design –
   • Async‑Pipe: producers enqueue data into a ring buffer; the runtime asynchronously triggers DMA while the consumer proceeds with computation, overlapping copy and compute.
   • Cache‑Inject: after a DMA transfer, the runtime injects selective cache lines to guarantee visibility without a full cache flush.
   • Hybrid‑Coord: combines software‑managed reference counting with hardware completion notifications to avoid lock contention.
5. Evaluation – The prototype runs on x86 servers equipped with Intel i225 NICs (supporting RDMA) and AMD EPYC CPUs. Benchmarks include a multimodal transformer serving pipeline, a video transcoding microservice, and a key‑value store using shared memory for request queues.

Results & Findings

• Size matters: For payloads > 256 KB, hardware DMA dominates and yields > 1.8× throughput gains.
• Latency‑critical paths (≤ 64 KB) benefit most from the Cache‑Inject mode, shaving off up to 45 µs of tail latency.
• CPU savings translate directly into lower cloud‑instance costs—roughly a 30 % reduction in required vCPU count for the same SLA.

Practical Implications

• Framework‑level integration – Languages and runtimes that already use shared memory (e.g., Rust’s mmap, Go’s syscall.Mmap) can plug the runtime as a drop‑in library, gaining offload benefits without rewriting IPC code.
• Microservice orchestration – Service meshes that rely on side‑car proxies can replace network‑bound JSON payloads with high‑throughput shared‑memory queues, offloading the bulk data movement to NIC‑RDMA or DMA engines.
• Cost‑effective scaling – Data‑center operators can pack more tenant workloads per host because the CPU budget previously spent on copies is reclaimed for compute.
• Edge AI deployments – Low‑power edge boxes (e.g., Jetson, Coral) often have limited CPU headroom; offloading IPC to the on‑board DMA reduces inference pipeline stalls.
• Observability – The runtime exposes metrics (offload latency, cache‑inject hit‑rate) via Prometheus, enabling automated tuning based on live traffic patterns.

Limitations & Future Work

• Hardware dependence – The biggest gains rely on NICs or DMA engines that support zero‑copy and completion notifications; older servers may see modest improvements.
• Security model – Sharing memory across processes still requires careful permission handling; the paper’s prototype assumes trusted co‑located services.
• Portability – Current implementation targets Linux x86; extending to ARM or Windows would need new driver hooks.
• Dynamic workload adaptation – The scheduler uses static thresholds; future work could incorporate machine‑learning models to predict optimal offload mode per request.

Bottom line: By treating memory copies as a first‑class, offloadable operation, this research opens a practical path for developers to squeeze more performance out of existing hardware, especially in data‑intensive AI services where IPC costs have become a hidden bottleneck.

Authors

• Misun Park
• Richi Dubey
• Yifan Yuan
• Nam Sung Kim
• Ada Gavrilovska

Paper Information

• arXiv ID: 2601.06331v1
• Categories: cs.OS, cs.DC
• Published: January 9, 2026
• PDF: Download PDF