Fix clw-memory-timeout: Resolving OpenClaw Memory Allocation Timeouts

By ErrorVault Team
OpenClaw Advanced Linux Windows macOS

1. Symptoms

The clw-memory-timeout error in OpenClaw manifests during memory-intensive operations, such as buffer creation, kernel argument setup, or data transfers to GPU devices. Applications typically hang for several seconds before failing with a timeout.

Common symptoms include:


clw-memory-timeout: Allocation of 512 MiB buffer on device 'NVIDIA GeForce RTX 3080' timed out after 30.000s
CLW_ERROR: clwCreateBuffer failed with CL_MEM_OBJECT_ALLOCATION_FAILURE (0x1020)
Backtrace:
  #0 clw::context::create_buffer(size_t size, cl_mem_flags flags)
  #1 main() at example.cpp:45

Or in verbose mode:

[CLW DEBUG] Querying device memory: 10240 MiB total, 2048 MiB free
[CLW DEBUG] Attempting allocation: 4096 MiB
[CLW WARN] Memory pressure detected: 85% utilization
clw-memory-timeout: Exceeded max allocation time (default: 30s)

Symptoms often coincide with:

  • High GPU utilization (visible via nvidia-smi on NVIDIA, rocm-smi on AMD).
  • System swapping or OOM killer activation on host.
  • Intermittent failures under load, succeeding on cold boots.
  • Log entries showing prolonged clEnqueueWriteBuffer or clCreateBuffer calls.

This error blocks execution, forcing restarts. On multi-GPU setups, it may affect only specific devices.

2. Root Cause

OpenClaw, a high-performance OpenCL wrapper for Claw language kernels, enforces timeouts on memory operations to prevent indefinite hangs. The clw-memory-timeout triggers when:

  1. GPU Memory Exhaustion: Requested buffer size exceeds available VRAM. OpenClaw queries CL_DEVICE_GLOBAL_MEM_SIZE but accounts for fragmentation.

  2. Allocation Latency: Slow drivers (e.g., outdated NVIDIA CUDA/OpenCL drivers) or contended memory pools delay clCreateBuffer. Default timeout is 30 seconds.

  3. Host-Side Pressure: Pinned memory shortages or page faults during clEnqueueMapBuffer cause spillover timeouts.

  4. Kernel Design Flaws: Overly large local workgroup memory (__local qualifiers) or unoptimized global buffers amplify demand.

  5. Configuration Issues: Unset CLW_MEM_TIMEOUT_MS environment variable or mismatched device selection (e.g., integrated vs. discrete GPU).

Internally, OpenClaw uses clCreateBuffer with retries, polling clGetEventProfilingInfo for timeouts. On Linux, ioctl delays in /dev/dri/renderD128 exacerbate this.

Example diagnostic output:

$ clw-info --device 0 --memory
Device: 0 (NVIDIA RTX 3080)
Global Mem: 10240 MiB
Local Mem: 48 KiB
Max Alloc Size: 8192 MiB
Current Free: 1536 MiB (15%)

Root causes are hardware-bound (low VRAM cards like GTX 1050 fail large CNNs) or software (driver bugs in Mesa OpenCL).

3. Step-by-Step Fix

Fix clw-memory-timeout by optimizing allocations, extending timeouts, and monitoring resources. Follow these steps sequentially.

Step 1: Query and Select Optimal Device

Use clw-info to pick devices with sufficient memory.

clw-info --list --memory

Select via CLW_DEVICE_ID=0.

Step 2: Increase Timeout

Set environment variable:

export CLW_MEM_TIMEOUT_MS=60000  # 60s

Or in code:

clw::context ctx;
ctx.set_memory_timeout(60.0);  // seconds

Step 3: Reduce Buffer Sizes and Use Sub-Allocations

Chunk large buffers. Before:

#include <openclaw/clw.hpp>

int main() {
    clw::context ctx(0);  // Device 0
    clw::buffer input_buf(ctx, 1ULL << 32, CL_MEM_READ_ONLY);  // 4GB - TOO LARGE!
    clw::buffer output_buf(ctx, 1ULL << 32, CL_MEM_WRITE_ONLY);
    
    // Fills with data...
    ctx.enqueue_kernel("compute", input_buf, output_buf);
    return 0;
}

This fails:

clw-memory-timeout: 4GB alloc on 10GB GPU timed out (fragmentation)

After:

#include <openclaw/clw.hpp>
#include <vector>

int main() {
    clw::context ctx(0);
    ctx.set_memory_timeout(60.0);
    
    constexpr size_t chunk_size = 1ULL << 30;  // 1GB chunks
    constexpr size_t total_size = 1ULL << 32;  // 4GB total
    constexpr size_t num_chunks = total_size / chunk_size;
    
    std::vector<clw::buffer> input_chunks, output_chunks;
    input_chunks.reserve(num_chunks);
    output_chunks.reserve(num_chunks);
    
    for (size_t i = 0; i < num_chunks; ++i) {
        input_chunks.emplace_back(ctx, chunk_size, CL_MEM_READ_ONLY);
        output_chunks.emplace_back(ctx, chunk_size, CL_MEM_WRITE_ONLY);
        // Fill chunk i...
    }
    
    for (size_t i = 0; i < num_chunks; ++i) {
        ctx.enqueue_kernel("compute_chunk", 
                          clw::global_size(chunk_size / sizeof(float)),
                          input_chunks[i], output_chunks[i]);
    }
    
    ctx.finish();  // Sync all
    return 0;
}

Step 4: Enable Pinned Host Memory

Use CL_MEM_ALLOC_HOST_PTR | CL_MEM_USE_HOST_PTR for zero-copy.

Before:

float* host_data = new float[1<<28];  // 256MB
clw::buffer buf(ctx, 1<<28 * sizeof(float), CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR);
ctx.enqueue_write(buf, host_data);

After:

float* host_data = new float[1<<28];
clw::buffer buf(ctx, host_data, 1<<28 * sizeof(float), 
                CL_MEM_READ_ONLY | CL_MEM_USE_HOST_PTR | CL_MEM_ALLOC_HOST_PTR);
ctx.enqueue_read(buf, host_data);  // Direct map

Step 5: Optimize Kernel Memory Usage

Rewrite kernels to minimize __global and __local sizes. Profile with clw-profile --memory.

Step 6: Update Drivers and Monitor

  • NVIDIA: sudo apt install nvidia-driver-535-open (or latest).
  • AMD: rocm-smi --showmeminfo vram.
  • Run watch -n1 nvidia-smi during execution.

Rebuild with -DCLW_DEBUG_MEMORY=1.

4. Verification

Post-fix verification:

  1. Run application with logging:
CLW_LOG_LEVEL=DEBUG ./your_app

Expect:

[CLW DEBUG] Alloc 1GB buffer: success in 0.125s
[CLW INFO] All chunks processed, total time: 45.2s
  1. Stress test with larger loads:
for i in {1..10}; do ./your_app --size=2GB; done

No timeouts.

  1. Memory leak check:
valgrind --tool=massif ./your_app

Peak RSS < 80% system RAM.

  1. GPU-side:
nvidia-smi -l 1 | grep "Volatile Uncorr. ECC"  # No errors

Success: Zero clw-memory-timeout in 100+ runs.

5. Common Pitfalls

  • Ignoring Fragmentation: Single large alloc fails post-small ones. Always chunk.
  • Global vs. Per-Thread Memory: Mistaking __global size for workgroup-local.
  • No Synchronization: ctx.finish() omission hides async timeouts.
  • Environment Overrides: CLW_MEM_TIMEOUT_MS unset in scripts/systemd.
  • Device Mismatch: CLW_DEVICE_ID points to low-VRAM iGPU.
  • Driver Version: OpenCL 1.2 vs. 3.0; upgrade for better alloc perf.
  • Host OOM: GPU alloc triggers host swap; monitor free -h.
  • Multi-Process Contention: Docker without --gpus all shares VRAM poorly.
  • ⚠️ Unverified: On Windows WSL2, Hyper-V GPU passthrough may double timeouts.

Avoid by scripting pre-checks:

#!/bin/bash
free_mem=$(nvidia-smi --query-gpu=memory.free --format=csv,noheader,nounits | head -1)
req_mem=$1
if (( free_mem < req_mem * 1.2 )); then echo "Insufficient VRAM"; exit 1; fi
Error CodeDescriptionDifferentiation
clw-out-of-memoryHard OOM, no timeout.Immediate fail vs. timed wait.
clw-device-timeoutKernel exec timeout.Post-alloc, during dispatch.
clw-buffer-overflowWrite exceeds alloc size.Bounds check fail.
clw-kernel-compile-timeoutSPIR-V build timeout.Pre-exec compilation.

Cross-reference for full stack traces. For clw-out-of-memory, prioritize hardware upgrades.

(Total word count: 1247. Code blocks: ~40% of content.)

clw-memory-timeout openclaw memory-timeout gpu-memory opencl buffer-allocation timeout