Low‑Latency GPU Packet Processing: Techniques, Trade‑offs, and Benchmarks

GPU Packet Processing Overview

Processing network packets on a GPU can dramatically increase throughput compared with a CPU‑only solution, but achieving low latency requires careful optimization of data movement, kernel launch overhead, batch size, synchronization, and memory access patterns.

Challenges of Low‑Latency GPU Processing

Data transfer overhead : Moving data between host and device memory is a primary bottleneck.

Kernel launch overhead : Each launch adds ~5‑10 µs.

Batching tension : Larger batches improve throughput but increase latency.

Synchronization cost : Coordination between CPU and GPU adds delay.

Memory access pattern : Irregular accesses can reduce cache efficiency.

Basic Packet‑Processing Pipeline

Packet capture : Receive packets from the network interface.

Batching : Group packets to amortize transfer and launch costs.

Transfer to GPU : Copy packet data into GPU memory.

Processing : Execute the core kernel on the GPU.

Result transfer : Copy results back to the host.

Response/forwarding : Take action based on the results.

network → CPU buffer → batch collection → GPU transfer → GPU processing → result transfer → action

Code Structure and Design

The implementation separates core packet‑processing logic from optimization strategies, enabling clear comparison of each technique.

Separation of concerns : Decouples processing logic from optimizations.

Ease of comparison : Same core logic is used across all experiments.

Maintainability : Optimizations can be added or removed independently.

Clarity : Impact of each optimization is visible.

Core Components

Packet

: Header, payload, size, and state. PacketResult: Processing outcome and required action. PacketBatch: Group of packets for batching. processPacketCPU(): CPU implementation of the core logic. processPacketGPU(): Device function implementing the same logic on the GPU.

Optimization Techniques

CPU vs. GPU Baseline

// CPU implementation
void processPacketCPU(const Packet* packet, PacketResult* result, int packetId) {
    // core packet processing logic
}

// GPU implementation
__device__ void processPacketGPU(const Packet* packet, PacketResult* result, int packetId) {
    // same core logic, executed on the device
}

The GPU version runs thousands of threads in parallel, whereas the CPU processes packets sequentially.

Pinned (Page‑Locked) Memory

Problem : Standard pageable memory requires extra copies when transferred to/from the GPU.

Solution : Allocate pinned memory to allow direct GPU access.

cudaHostAlloc(&h_packets, packet_buffer_size, cudaHostAllocDefault);

Benefit : Host‑to‑device transfer speed roughly doubles.

Zero‑Copy Memory

Problem : Even with pinned memory, explicit transfers add latency.

Solution : Map host memory into the GPU address space.

cudaHostAlloc(&h_packets, packet_buffer_size, cudaHostAllocMapped);
cudaHostGetDevicePointer(&d_packets, h_packets, 0);

Benefit : Eliminates explicit copies, enabling fine‑grained access.

Trade‑off : Lower PCIe bandwidth can increase latency for large transfers.

Batching Strategies

Timeout‑based batching : Process when a timeout expires or the batch is full.

Dynamic batch size : Adjust batch size based on load and latency requirements.

Two‑level batching : Small batches for critical packets, larger batches for the rest.

Stream Concurrency

Problem : Serial execution of transfers and kernels wastes time.

Solution : Overlap operations using CUDA streams.

cudaStream_t streams[NUM_STREAMS];
for (int i = 0; i < NUM_STREAMS; i++) {
    cudaStreamCreate(&streams[i]);
}

for (int i = 0; i < NUM_BATCHES; i++) {
    int stream_idx = i % NUM_STREAMS;
    cudaMemcpyAsync(d_packets[i], h_packets[i], batch_size,
        cudaMemcpyHostToDevice, streams[stream_idx]);
    processPacketsKernel<<<grid, block, 0, streams[stream_idx]>>>(d_packets[i], d_results[i], batch_size);
    cudaMemcpyAsync(h_results[i], d_results[i], result_size,
        cudaMemcpyDeviceToHost, streams[stream_idx]);
}

Benefit : Improves throughput and reduces average latency.

Persistent Kernel

Problem : Kernel launch overhead adds significant latency.

Solution : Keep a kernel running indefinitely, pulling new work from a queue.

__global__ void persistentKernel(volatile int* work_queue, volatile int* queue_size,
                                 PacketBatch* batches) {
    while (true) {
        if (threadIdx.x == 0 && blockIdx.x == 0) {
            while (*queue_size == 0);
            batch_idx = atomicAdd((int*)queue_size, -1);
        }
        __shared__ int s_batch_idx;
        if (threadIdx.x == 0) s_batch_idx = batch_idx;
        __syncthreads();
        processPacketGPU(&batches[s_batch_idx].packets[tid], &results[tid], tid);
        if (threadIdx.x == 0 && blockIdx.x == 0) {
            batches[s_batch_idx].status = COMPLETED;
        }
    }
}

Benefit : Eliminates kernel launch overhead, achieving sub‑microsecond latency.

CUDA Graphs

Problem : Even with streams, each kernel launch incurs CPU overhead.

Solution : Capture the entire workflow as a CUDA graph and replay it.

cudaGraph_t graph;
cudaGraphExec_t graphExec;
cudaStreamBeginCapture(stream, cudaStreamCaptureModeGlobal);
for (int i = 0; i < PIPELINE_DEPTH; i++) {
    cudaMemcpyAsync(...); // input copy
    kernel<<<...>>>(...); // processing
    cudaMemcpyAsync(...); // output copy
}
cudaStreamEndCapture(stream, &graph);
cudaGraphInstantiate(&graphExec, graph, NULL, NULL, 0);
for (int batch = 0; batch < NUM_BATCHES; batch++) {
    updateGraphInputs(batch);
    cudaGraphLaunch(graphExec, stream);
}

Benefit : Reduces CPU overhead by 30‑50%, lowering overall latency.

Performance Analysis

The following metrics were measured for each method: end‑to‑end latency, throughput, batch processing time, transfer overhead, kernel execution time, and queue wait time.

CPU (baseline) : 6,639 µs (sequential processing).

Basic GPU : 4,124 µs (≈1.6× faster than CPU).

Pinned memory : 2,987 µs (≈2.2× faster than CPU).

Batching stream : 8,488 µs total, but per‑packet latency drops to 0.83 µs.

Zero‑copy : 61,170 µs (slow due to PCIe bandwidth limits).

Persistent kernel : 200,470 µs (high total time, includes simulated arrival latency).

CUDA graph : 132,917 µs (reduces launch overhead, still has synchronization cost).

Conclusion

Minimize data transfers wherever possible.

Use persistent kernels or CUDA graphs to cut kernel launch overhead.

Apply intelligent batching based on traffic patterns.

Employ stream pipelines to hide latency.

Leverage GPU‑specific memory features (pinned, zero‑copy) when appropriate.

By separating core processing from optimization strategies, the impact of each technique becomes clear, allowing selection of the best combination for a given workload.

References

NVIDIA CUDA Programming Guide: https://docs.nvidia.com/cuda/cuda-c-programming-guide/

NVIDIA GPUDirect: https://developer.nvidia.com/gpudirect

DPDK (Data Plane Development Kit): https://www.dpdk.org/

NVIDIA DOCA SDK: https://developer.nvidia.com/networking/doca