Diagnosing Unexplained 5-6GB Memory Usage on Amazon EC2 GPU Instances: Linux Process Investigation Guide


3 views

When running AWS EC2's Cluster Compute (cc1.4xlarge) instances with 22GB RAM, many users notice consistent 5-6GB memory consumption even on idle systems. Standard tools like top and ps aux fail to identify the responsible processes. Let's explore comprehensive diagnostic approaches.

First, try these specialized Linux memory analysis commands:


# Detailed memory breakdown
cat /proc/meminfo | grep -E 'MemTotal|MemFree|Buffers|Cached|Slab|SReclaimable|SUnreclaim'

# Kernel slab allocator statistics
sudo slabtop -o

# Page cache examination
sudo sync; echo 3 > /proc/sys/vm/drop_caches
free -h

Xen virtualization (used by AWS) does consume some resources:


# Check Xen balloon driver memory
grep -i balloon /proc/xen/*

Typical overhead includes:

  • Xen hypervisor reserved memory (~2-3GB)
  • DMA buffers and IOMMU mappings
  • PCI passthrough for GPU devices

GPU compute instances have special considerations:


# Check NVIDIA driver memory usage
nvidia-smi -q | grep -i memory

# Alternative using process explorer
sudo apt install htop
htop --sort-key=RES

Run this systematic check:


#!/bin/bash
echo "===== SYSTEM MEMORY OVERVIEW ====="
free -h

echo "\n===== PROCESS MEMORY USAGE ====="
ps -eo pid,ppid,cmd,%mem,%cpu --sort=-%mem | head -n 15

echo "\n===== KERNEL SLAB INFO ====="
cat /proc/meminfo | grep Slab

echo "\n===== HugePages STATUS ====="
grep -i huge /proc/meminfo

echo "\n===== GPU MEMORY USAGE ====="
nvidia-smi

In one investigation, we found:

  • 2.1GB - Xen balloon driver
  • 1.7GB - NVIDIA driver and CUDA context
  • 1.2GB - Kernel slab allocations
  • 0.8GB - Filesystem cache

The sum explained the observed memory usage pattern.

Consider action if:

  • Memory usage grows unexpectedly during operation
  • You observe OOM killer activity in dmesg
  • Available memory drops below your application requirements

When working with Amazon EC2 GPU instances (particularly Cluster Compute nodes), many developers notice a significant portion of RAM appears to be in use even when the system is idle. In your case, about 5-6GB of the 22GB total memory shows as used, but standard tools like top and ps aux don't reveal the culprit processes.

Several factors could explain this behavior:

  • Kernel and System Processes: The Linux kernel itself consumes memory for various subsystems.
  • GPU Driver Overhead: NVIDIA drivers often allocate substantial memory for GPU management.
  • Filesystem Caching: Linux aggressively caches files in unused memory.
  • Virtualization Overhead: AWS's hypervisor layer requires some memory allocation.

Standard process viewers won't show all memory usage. Try these more powerful alternatives:

# Check detailed memory breakdown
sudo cat /proc/meminfo

# Examine slab memory usage
sudo slabtop -o

# View memory mapping by process
sudo pmap -x $(pgrep -f "process_name")

# Check GPU memory usage
nvidia-smi

Linux uses available memory for disk caching. This shows as "used" memory but is actually available if applications need it:

free -h
              total        used        free      shared  buff/cache   available
Mem:            22G        5.8G        2.1G        456M         14G         15G

In this example, while 5.8G shows as used, 14G is buff/cache that can be reclaimed.

GPU instances often have hidden memory allocations:

# Check NVIDIA driver memory usage
cat /proc/driver/nvidia/gpus/0000:00:00.0/information

Kernel memory isn't always visible in process lists. Use:

# Check kernel memory usage
cat /proc/meminfo | grep Slab
sudo cat /proc/slabinfo

Here's a complete script to identify all memory usage:

#!/bin/bash

echo "===== Memory Overview ====="
free -h

echo "\n===== Detailed Memory Breakdown ====="
cat /proc/meminfo | egrep 'MemTotal|MemFree|Buffers|Cached|Slab|SReclaimable|SUnreclaim'

echo "\n===== Top Memory Processes ====="
ps -eo pid,ppid,cmd,%mem,%cpu --sort=-%mem | head -n 10

echo "\n===== GPU Memory Usage ====="
nvidia-smi

echo "\n===== Kernel Slab Memory ====="
sudo slabtop -o | head -n 20

5-6GB usage on a 22GB system is typically normal for EC2 GPU instances. However, investigate if:

  • Usage grows unexpectedly over time
  • Available memory drops below what your applications need
  • You observe performance degradation

If you need to minimize memory overhead:

# Reduce filesystem cache (temporary measure)
echo 3 | sudo tee /proc/sys/vm/drop_caches

# Use a lighter-weight Linux distribution