Fix clw-memory-failure: OpenClaw Memory Allocation Failure During Initialization

1. Symptoms

The clw-memory-failure error in OpenClaw manifests during initialization phases, such as context creation, device enumeration, or buffer allocation. This error indicates that the library failed to allocate sufficient host memory for internal structures.

Typical scenarios include:

[OpenClaw Error] clw-memory-failure at clw_context_create: Failed to allocate memory for device list (requested 1024 KB)

Or during program building:

CLW_ERROR(clw-memory-failure): Memory allocation failed in clw_program_build. System RAM low.

Symptoms often accompany high memory usage from other processes, large GPU device lists (e.g., multi-GPU setups), or when running on resource-constrained systems like laptops with integrated graphics.

In code, it throws an exception or returns `CLW_ERROR_MEMORY_FAILURE`:

```cpp
#include <openclaw/clw.hpp>

try {
    clw::Platform platforms = clw::Platform::getPlatforms();
    // ... further init
} catch (const clw::Error& e) {
    if (e.code() == CLW_ERROR_MEMORY_FAILURE) {
        std::cerr << "clw-memory-failure: " << e.what() << std::endl;
    }
}

Logs may show preceding warnings like “High memory pressure detected” or OpenCL ICD loader issues. Performance degrades before failure: slow device queries or hangs during clw::Context::create.

On Linux, check dmesg for OOM killer:

[12345.678] Out of memory: Kill process 1234 (your_app) score 900 or sacrifice child

Windows Event Viewer logs “Application error: EXCEPTION_ACCESS_VIOLATION” tied to memory exhaustion. macOS Console.app shows “malloc: can’t allocate region”.

2. Root Cause

OpenClaw, a lightweight C++ wrapper for OpenCL, relies on host-side memory allocations for:

• Device and platform metadata storage (e.g., cl_device_id arrays).
• Internal buffers for command queues, programs, and kernels.
• String storage for device names, extensions, and build logs.

clw-memory-failure triggers when malloc, new, or std::vector::reserve fails due to:

1. System Memory Exhaustion: Host RAM < 4GB free, especially with large OpenCL platforms (NVIDIA CUDA-OpenCL, AMD ROCm, Intel oneAPI).
2. Fragmented Heap: Long-running apps with many allocations leak memory, causing fragmentation.
3. Multi-GPU Overload: Enumerating 8+ GPUs allocates ~1-2MB per device for props.
4. Large Contexts: Creating contexts with all devices + properties like CL_CONTEXT_PLATFORM.
5. ICD Loader Overhead: OpenCL Installable Client Driver (ICD) loads multiple drivers, consuming 100-500MB.
6. 32-bit Builds: Address space limits (2-4GB) on 64-bit OS.

Underlying OpenCL call often clGetPlatformIDs or clCreateContext propagates CL_OUT_OF_HOST_MEMORY, mapped to CLW_ERROR_MEMORY_FAILURE.

Profile with Valgrind (Linux) or Dr. Memory (Windows):

==1234== 1,024,000 bytes in 1 blocks are definitely lost

Or top/htop shows swap usage >50%.

3. Step-by-Step Fix

Step 1: Verify System Resources

Run free -h (Linux/macOS) or Task Manager (Windows). Ensure >2GB free RAM. Close browsers/large apps.

Step 2: Limit Platforms and Devices

Filter to essential platforms/devices to reduce allocations.

Before:

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

int main() {
    try {
        auto platforms = clw::Platform::getPlatforms();  // Enumerates ALL
        auto devices = clw::Device::getDevices(platforms[0], CL_DEVICE_TYPE_ALL);
        auto context = clw::Context::build(devices);  // Allocates for all devices
        std::cout << "Success" << std::endl;
    } catch (const clw::Error& e) {
        std::cerr << e.what() << std::endl;  // clw-memory-failure here
    }
    return 0;
}

After:

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

int main() {
    try {
        auto platforms = clw::Platform::getPlatforms();
        std::vector<clw::Device> devices;
        
        // Filter: First GPU only, skip CPU/accelerators to save memory
        for (const auto& plat : platforms) {
            if (plat.name().find("NVIDIA") != std::string::npos ||  // Or AMD/Intel
                plat.name().find("AMD") != std::string::npos) {
                auto gpu_devs = clw::Device::getDevices(plat, CL_DEVICE_TYPE_GPU);
                if (!gpu_devs.empty()) {
                    devices.push_back(gpu_devs[0]);  // Single device
                    break;
                }
            }
        }
        
        if (devices.empty()) {
            throw std::runtime_error("No suitable GPU found");
        }
        
        auto context = clw::Context::build(devices);
        std::cout << "Context created with 1 device" << std::endl;
    } catch (const clw::Error& e) {
        std::cerr << "OpenClaw Error: " << e.what() << std::endl;
    } catch (const std::exception& e) {
        std::cerr << "Std Error: " << e.what() << std::endl;
    }
    return 0;
}

Step 3: Use 64-bit Builds and Custom Allocators

Compile with -m64. For advanced, hook new with jemalloc:

# Linux: Install jemalloc
apt install libjemalloc-dev
g++ -std=c++17 -O2 main.cpp -lOpenCL -ljemalloc -o app

Before (leaky):

clw::Buffer buf(context, CL_MEM_READ_WRITE, 1ULL << 30);  // 1GB, no check

After:

size_t max_size = 1ULL << 28;  // 256MB cap
if (available_ram() < max_size * 2) {  // Pseudo-check
    max_size /= 4;
}
clw::Buffer buf(context, CL_MEM_READ_WRITE, max_size);

Step 4: Release Early

Explicitly release:

{
    auto scope_ctx = clw::Context::build({device});
    // Use scope_ctx
}  // Auto-release on scope exit

Step 5: Environment Tweaks

Set CL_CONFIG_DISABLE_CACHE=1 to skip build cache allocations. On NVIDIA, export CUDA_VISIBLE_DEVICES=0.

4. Verification

1. Recompile and run fixed code. Expect “Context created” without errors.
2. Monitor memory: watch free -h or PerfMon.
3. Stress test: Allocate multiple buffers.

// Test loop
for (int i = 0; i < 10; ++i) {
    auto buf = clw::Buffer(context, CL_MEM_READ_WRITE, 100 * 1024 * 1024);
    buf.fill(0);
}

4. Valgrind check:

valgrind --tool=memcheck --leak-check=full ./app
==1234== ERROR SUMMARY: 0 errors

5. OpenClaw logs: Set CLW_LOG_LEVEL=debug env var for verbose output confirming allocations.

Success: No clw-memory-failure, context props query succeeds.

5. Common Pitfalls

• Ignoring Return Codes: Blind clw::Context::build() without try-catch propagates silently.
• All Devices: CL_DEVICE_TYPE_ALL explodes memory on servers (32+ GPUs).
• String Ops: device.name() or buildLog() allocates strings; cache them.
• 32-bit: Forces small allocs; always 64-bit.
• No Swap: Disable swap worsens; enable 4GB+ swapfile.
• Parallel Init: Multi-threaded getPlatforms() races allocations.
• Leaky Loops: Repeated context creation without release.

⚠️ Unverified on ROCm 6+: May need HIP_VISIBLE_DEVICES.

Profiling tip: clw::Device::getDevices with props mask CL_DEVICE_IMAGE_SUPPORT skips extras.

6. Related Errors

• clw-invalid-context: Context null post-failure. Fix: Check context() before use.
• clw-out-of-host-memory: OpenCL native; mirrors this but lower-level.
• clw-device-not-found: No devices after memory filter; add fallbacks.
• clw-invalid-device: Stale device post-context fail; recreate.

Cross-reference for multi-error chains.

(Word count: 1,256. Code blocks: ~45%)