Zero-Copy Streaming Architecture

• Zero-copy streaming architecture is a distributed data movement paradigm that eliminates redundant data copies, minimizing CPU overhead and enhancing throughput.
• It leverages hardware DMA, shared memory mapping, and kernel techniques to streamline data transfers in high-performance computing and analytics.
• Practical applications in HPC networks, serverless data planes, and neural rendering pipelines demonstrate significant latency reductions and energy savings.

A zero-copy streaming architecture is a distributed data movement paradigm that eliminates data copies during communication, computation, or storage, allowing devices and processes to directly access application memory or shared buffer regions. Contemporary zero-copy architectures underpin a wide range of high-performance systems, from HPC networks to serverless cloud data planes, neural rendering pipelines, and robotics middleware. The central goals are minimizing memory traffic, reducing CPU overhead, and attaining line-rate throughput—often by co-designing hardware and software interfaces to leverage direct memory access (DMA), shared memory mapping, and protocol-level optimizations.

1. Foundational Principles of Zero-Copy Streaming

Zero-copy streaming architectures are founded on avoiding redundant buffer copying. In classic networked systems, data passes from application buffers into kernel buffers, then into device buffers, incurring performance loss and wasting memory bandwidth. Zero-copy approaches rely on the capability of hardware (NICs, DPUs) or the OS to directly map application-specified memory regions for outgoing and incoming messages. For example, high-performance networking hardware (e.g. Infiniband) can send and receive data at speeds matching the local memory bus by performing DMA directly to/from application pages (Power, 2013).

Zero-copy streaming is crucial wherever memory bandwidth is a limiting factor—such as in distributed data analytics ("Zerrow" (Dai et al., 8 Apr 2025)), neural rendering accelerators ("Potamoi" (Feng et al., 13 Aug 2024)), or robotics IPC ("Agnocast" (Ishikawa-Aso et al., 20 Jun 2025)). The paradigm reduces serialization/deserialization overhead, write amplification, and volatility in latency.

2. OS- and Kernel-Level Techniques

Many zero-copy mechanisms rely on OS features to protect, share, or remap memory regions dynamically. The memory protection approach (Power, 2013) leverages hardware mechanisms (typically via the mprotect syscall) to mark buffers enrolled in a zero-copy network operation as read-only. Any attempted write triggers a segmentation fault, caught by a signal handler, which then monitors the status of the send operation and lifts the protection when complete. In LaTeX notation, for a buffer pointer $ptr$ and page size $P$, the protected region is:

$$\text{page\_start} = \left\lfloor \frac{\text{ptr}}{P} \right\rfloor \times P$$

$$\text{prot\_len} = \text{buffer.size} + (\text{ptr} - \text{page\_start})$$

The handler ensures safe, implicit locking: it yields until the MPI or RDMA operation's completion, and then enables writes again. The granularity constraint—that mprotect can only work on page-aligned regions—may introduce false blocking for adjacent, unrelated buffers, motivating hybrid strategies for small-message handling.

Linux kernel manipulation is key in Arrow-based pipelines, where anonymous memory regions are unsuitable for sharing. "Zerrow" (Dai et al., 8 Apr 2025) introduces KernelZero, a module which de-anonymizes memory: user routines allocate buffers via malloc, but KernelZero walks the process's page table, reassigns metadata, and links the region to a tmpfs file so downstream processes can map the same physical pages as a file-backed mapping—no data copy occurs.

3. Hardware Offloading and NIC/Accelerator Co-Design

Modern NICs and DPUs present programmable units with high-level packet streaming interfaces (sPIN model (Girolamo et al., 2019), DOCA APIs (Qi et al., 16 May 2025)) that can offload both data movement and layout transformation. For non-contiguous memory transfers, packet streaming processors (NIC HPUs) handle per-packet operations, using user-defined handlers to scatter/gather message blocks directly via DMA into target memory regions. The sequence for an MPI vector type is:

• Compute destination addresses: $$host\_offset = \left\lfloor \frac{pkt\_offset}{block\_size} \right\rfloor \times stride;~~ host\_address = host\_base\_ptr + host\_offset$$
• DMA-write each block in the packet: $$DMA\_write(pkt\_payload, block\_size) \rightarrow (host\_address)$$

Two-sided RDMA operations, as in Palladium (Qi et al., 16 May 2025), further enforce receiver-side buffer readiness, preventing races and eliminating the need for locks or per-packet copying. The architecture offloads transport and buffer management tasks to DPU cores, which operate over cross-processor shared memory pools with event-driven processing loops—minimizing CPU and scheduling costs.

Performance metrics from Palladium illustrate the impact: throughput improvement (requests-per-second) of ~20.9%, latency decreased by up to 21$\times$, and freeing up to 7 host CPU cores by using only 2 DPU cores.

4. Advanced Shared Memory Mapping and Data Format Adaptation

Cluster shared memory architectures for data analytics exploit both software and hardware interfaces to permit cross-node direct access to distributed tables. In "Leveraging Apache Arrow for Zero-copy, Zero-serialization Cluster Shared Memory" (Groet et al., 3 Apr 2024), Arrow's immutable, offset-based columnar format supports exchanging just table descriptors (metadata + pointers). By mapping shared buffers into the same virtual address space (using Linux mmap and MAP_FIXED flags), all nodes see globally valid pointers:

$P_i = \text{fixed\_address} + \text{offset}_i$

Cache coherence is partial—remote caches are not updated globally—but Arrow’s immutability guarantees correctness. Cache lines must be explicitly flushed before buffer initialization; after this, subsequent memory reads are direct and zero-copy.

In "Zerrow" (Dai et al., 8 Apr 2025), the architecture further refines sharing granularity (column level, with future support for batch-level sharing), and extends Arrow’s IPC protocol to write out tuples $(file\_id, offset, length)$ rather than full record batches. SIPC module records the memory ranges used and performs resharing (reuse of file references for output regions overlapping with input). Dictionary encoding operations reuse dictionaries via resharing, minimizing both output sizes and internal memory use.

5. Dataflow, Streaming Order, and Application-specific Optimizations

Zero-copy streaming is not merely a transport technique; it can reshape computation pipelines. In Potamoi (Feng et al., 13 Aug 2024), the pipeline for neural radiance fields (NeRFs) is reorganized from a pixel-centric (scatter/gather) order to a memory-centric streaming architecture. Spatially contiguous features (MVoxels) are grouped and loaded sequentially from DRAM; associated samples are processed together, yielding "one-read–many-uses" reuse. DRAM traffic and SRAM bank conflicts are minimized, as channel-major layouts interleave channels across banks to support parallel access without conflict.

SpaRW (Sparse Radiance Warping) utilizes radiance similarity across views to cut 88% of GEMM computations by reusing reference frame pixel values and only recomputing for “hole” pixels (disocclusions). The net outcome is dramatic: a speedup of 53.1$\times$ and energy saving of 67.7$\times$ versus conventional DNN accelerator baselines, with visual quality preserved to within 1.0 dB PSNR loss.

See Table 1 for a summary of selected architectural features and performance:

Architecture	Approach	Key Metric
Palladium (Qi et al., 16 May 2025)	DPU offloading + 2-sided RDMA	20.9% RPS, 21$\times$ latency reduction
Potamoi (Feng et al., 13 Aug 2024)	Memory-centric streaming + SpaRW	53.1$\times$ speedup, 67.7$\times$ energy reduction
Zerrow (Dai et al., 8 Apr 2025)	Kernel de-anonymization, SIPC IPC	3.9$\times$ writer speedup, 2.8$\times$ throughput via DeCache
Agnocast (Ishikawa-Aso et al., 20 Jun 2025)	Heap interception + shared mapping	Constant IPC overhead, 16% avg/25% worst-case latency improvement

6. Robustness, Integration, and Selective Adoption

Zero-copy streaming systems require careful management of buffer protection, memory mapping, and resource allocation—particularly to support multi-tenancy (Palladium), mixed IPC modes (Agnocast), or resource-constrained edge devices (Potamoi). Robustness demands kernel-level modules for tracking reference counts and buffer use (Agnocast), adaptive resource managers for eviction and admission control (Zerrow), and protocol conversion logic for integrating with external clients or legacy systems (Palladium).

Selective adoption is supported by bridging processes (Agnocast bridge), which can relay messages between conventional communication middleware and zero-copy channels, easing migration and incremental deployment across heterogeneous application landscapes.

7. Prospects, Research Directions, and Limitations

A plausible implication is that future research will address finer memory region granularity (sub-column/batch sharing), improved kernel/OS APIs for memory mapping (e.g., process_vm_mmap, msharefs), and more efficient cache coherence across cluster nodes. Mutability and granularity remain limitations where current page/protection or mapping schemes are too coarse. Cache flush operations and mapping constraints (MAP_FIXED) incur setup overheads in cluster environments; bandwidth to remote memory is inherently limited versus local host DRAM.

Zero-copy is also more challenging for unsized or dynamically allocated data structures, as in robotic middleware. Agnocast (Ishikawa-Aso et al., 20 Jun 2025) overcomes this via LD_PRELOAD heap interception and shared kernel-managed metadata, but notes the need for further selective mapping and dynamic address negotiation.

References to Key Literature

• Using Memory-Protection to Simplify Zero-copy Operations (Power, 2013)
• Network-Accelerated Non-Contiguous Memory Transfers (Girolamo et al., 2019)
• Leveraging Apache Arrow for Zero-copy, Zero-serialization Cluster Shared Memory (Groet et al., 3 Apr 2024)
• Potamoi: Accelerating Neural Rendering via a Unified Streaming Architecture (Feng et al., 13 Aug 2024)
• Zerrow: True Zero-Copy Arrow Pipelines in Bauplan (Dai et al., 8 Apr 2025)
• Palladium: A DPU-enabled Multi-Tenant Serverless Cloud over Zero-copy Multi-node RDMA Fabrics (Qi et al., 16 May 2025)
• ROS 2 Agnocast: Supporting Unsized Message Types for True Zero-Copy Publish/Subscribe IPC (Ishikawa-Aso et al., 20 Jun 2025)

Zero-copy streaming architecture has evolved from a niche optimization in HPC and networking to a broad, unifying paradigm across serverless, analytics, robotics, and neural rendering. The ongoing direction is toward more general, fine-grained, and robust mechanisms for eliminating data copies in increasingly complex distributed and heterogeneous systems.