Great question! CUDA doesn’t offer a native way to synchronize an arbitrary subset of warps in a block directly, but we can work around this by managing data dependencies explicitly and using selective synchronization techniques to avoid waiting on warps whose data isn’t needed immediately. Let’s break down how to address your specific scenario:

Your Scenario Recap #

Block-wide thread flow: Global memory → shared buffer → __syncthreads() → work on shared buffer → __syncthreads() → ...
After the first sync, warps 3-5’s copied data isn’t needed yet—you want to skip waiting for their global memory copies to finish to hide latency.

Key Approaches to Achieve This #

1. Split Shared Memory & Isolate Dependencies #

First, partition your shared memory into two separate regions:

• One for data needed immediately (warps 0-2’s data)
• Another for data needed later (warps 3-5’s data)

By separating these regions, you can decouple the copy operations:

• Warps 0-2 copy their data to the "immediate use" region first
• Warps 3-5 can execute their copy operations in parallel with warps 0-2’s subsequent work, rather than being forced to finish before the entire block proceeds.

For intra-warp synchronization (to ensure all threads in a warp finish their copy), use __syncwarp(). If you need cross-warp visibility within the immediate-use group (e.g., warp 0 needs to read warp 1’s shared data), use __threadfence_block() to guarantee memory visibility across the block, then use a shared memory flag to signal completion instead of a full __syncthreads().

2. Selective Synchronization with Shared Memory Flags #

Since __syncthreads() requires all threads in the block to reach the sync point, we can’t use it directly for partial warp sync. Instead, use shared memory flags to track which warps have finished their critical operations, and only have the relevant warps wait for those flags.

Here’s a simplified code example:

__shared__ float shared_immediate[3*32];  // For warps 0-2
__shared__ float shared_deferred[3*32];   // For warps 3-5
__shared__ int warp_ready[6];              // Track completion for warps 0-5

const int warp_id = threadIdx.x / 32;
const int lane_id = threadIdx.x % 32;

// Split copy operations by warp group
if (warp_id <= 2) {
    // Copy data needed immediately
    shared_immediate[warp_id*32 + lane_id] = global_data[...];
    __syncwarp();  // Ensure all threads in this warp finish copying
    
    // Mark this warp as ready (only one thread per warp needs to write)
    if (lane_id == 0) {
        warp_ready[warp_id] = 1;
    }
} else if (warp_id >= 3 && warp_id <=5) {
    // Copy deferred data (can run in parallel with immediate work)
    shared_deferred[(warp_id-3)*32 + lane_id] = global_data[...];
    
    if (lane_id == 0) {
        warp_ready[warp_id] = 1;
    }
}

// Only warps 0-2 wait for their group to be ready
if (warp_id <=2) {
    // Wait until all warps 0-2 have marked themselves ready
    while (__syncthreads_count(warp_ready[0] & warp_ready[1] & warp_ready[2]) == 0);
    
    // Now safely work with shared_immediate data
    // ... your immediate work here ...
}

// When you eventually need warps 3-5's data, sync appropriately
// e.g., a full __syncthreads() or another selective wait

Note: __syncthreads_count() requires all threads in the block to execute it, but warps outside the 0-2 group will just loop once (since their condition doesn’t require waiting) and proceed with other work.

3. Leverage Hardware Latency Hiding #

CUDA SMs automatically hide memory latency by switching between warps when one is stalled (e.g., waiting for global memory). If you remove the initial __syncthreads() entirely:

• Warps 0-2 will finish their copies and start executing their shared memory work immediately
• Warps 3-5 will continue their global memory copies in the background, with the SM scheduling their execution alongside the work of warps 0-2

This works perfectly as long as your immediate work doesn’t access the deferred shared memory region—since you’ve stated that data isn’t needed yet, this is a clean way to let the hardware handle latency hiding without extra synchronization.

Final Takeaway #

While there’s no direct "partial warp sync" primitive in CUDA, you can achieve your goal by:

• Isolating data dependencies to avoid unnecessary synchronization
• Using warp-level sync (__syncwarp()) and shared memory flags for selective waits
• Letting the hardware’s warp scheduling hide latency by avoiding over-synchronization with __syncthreads()

内容的提问来源于stack exchange，提问作者tmlen