#Paddle Autogrow Memory Allocation Crash on GPU-Manager

#Problem Description

#Symptoms

When both PaddlePaddle's Autogrow memory allocation strategy and GPU-Manager's virtualized memory management are enabled simultaneously, the following anomalies may occur:

1. OOM errors due to discontinuous memory allocation
2. Abnormal GPU utilization fluctuations
3. Random training process crashes
4. Inconsistent memory usage between nvidia-smi reports and framework statistics

#Root Cause

#Root Cause Analysis

1. Memory Allocation Strategy Conflict Paddle's Autogrow uses dynamic segmented allocation while GPU-Manager's virtualization requires contiguous physical memory mapping

2. Management Mechanism Incompatibility Autogrow's delayed release mechanism conflicts with GPU-Manager's memory reclamation strategy

3. Metadata Maintenance Conflict Separate metadata maintenance by both systems causes inconsistent memory views

Trigger Mechanism:

• Autogrow attempts optimal block sizing during allocation
• GPU-Manager virtualization layer intercepts physical memory requests
• Non-contiguous allocations cause virtual address mapping failures
• Dual management leads to metadata consistency exceptions

#Solution

#Solution Overview

Force Paddle to use traditional allocation strategy via environment variable:

FLAGS_allocator_strategy=naive_best_fit

#Considerations

1. Requires training process restart
2. May reduce Paddle's memory reuse efficiency

#Implementation Steps

#Kubernetes Deployment

1. Edit Deployment configuration

   apiVersion: apps/v1
   kind: Deployment
   spec:
     template:
       spec:
         containers:
           - name: paddle-container
             env:
               - name: FLAGS_allocator_strategy
                 value: 'naive_best_fit'

2. Apply configuration

   kubectl apply -f updated_deployment.yaml

3. Verify configuration

   kubectl exec <pod-name> -- env | grep FLAGS

#Bare Metal Deployment

1. Set environment variable before execution

   export FLAGS_allocator_strategy=naive_best_fit
   python train.py

2. Or set in Python code

   import os
   os.environ['FLAGS_allocator_strategy'] = 'naive_best_fit'

#Verification Methods

1. Check allocation strategy confirmation in logs

   I0715 14:25:17.112233 12345 allocator.cc:256]
   Using Naive Best Fit allocation strategy

2. Monitor memory allocation continuity

   nvidia-smi --query-gpu=memory.used --format=csv -l 1

3. Stress test validation

   # Continuous allocation test script
   import paddle
   for i in range(10):
       data = paddle.randn([1024, 1024, 100], dtype='float32')
       print(f"Allocated {i+1}GB")

#Preventive Measures

1. Version Compatibility Check Review Paddle release notes for memory allocation changes during upgrades

2. Monitoring Configuration Add Prometheus alert rule:

   • alert: GPUAllocConflict
     expr: rate(paddle_gpu_malloc_failed_total[5m]) > 0
     labels:
       severity: critical
     annotations:
       summary: "GPU Memory Allocation Conflict Alert"

3. Baseline Testing Perform memory allocation baseline tests for new environments:

   python -c "import paddle; paddle.utils.run_check()"

#Related Content

#Memory Allocation Strategy Comparison

#References

Paddle Memory Optimization Whitepaper