GPerf CPU Profiling Environment

The GPerf CPU Profiling Environment plugin provides CPU usage monitoring and profiling capabilities for services running within PANTHER. It uses Google's gperftools to collect detailed CPU profiling information, helping identify performance bottlenecks and optimization opportunities in implementations under test.

Plugin Information

Plugin Type: Execution Environment Source Location: plugins/environments/execution_environment/gperf_cpu/

Performance Analysis

This plugin is particularly useful for:

• Identifying Bottlenecks: Find CPU-intensive operations in protocol implementations
• Benchmarking: Compare performance across different implementation strategies
• Regression Detection: Monitor performance changes across versions
• Resource Optimization: Optimize CPU usage in protocol stacks

Requirements and Dependencies

The plugin requires:

• gperftools: Google Performance Tools library (often available as google-perftools package)
• pprof: Performance analysis tool for visualization (included with gperftools)
• graphviz: (Optional) For generating visual profile reports

System dependencies can be installed on Ubuntu/Debian systems with:

sudo apt-get install google-perftools libgoogle-perftools-dev graphviz

Configuration Options

The GPerf CPU environment accepts the following configuration parameters:

execution_environment:
  - type: "gperf_cpu"
    input_file: "input.txt"        # Optional input for the profiled application
    output_file: "cpu.profile"     # Output profile file name
    language: "C++"                # Programming language
    readonly_tables: true          # Generate read-only tables
    includes: ["<string>", "<vector>"] # Include files
    other_flags: ["-v", "--debug"] # Additional gperf flags

Parameter	Type	Required	Default	Description
type	string	Yes	-	Must be "gperf_cpu"
input_file	string	No	None	Input file for the profiled application
output_file	string	No	None	Name of the output profile file
language	string	No	"C"	Programming language of the application
keyword_only	boolean	No	false	Generate keyword-only lookup
readonly_tables	boolean	No	false	Generate read-only tables
switch	boolean	No	false	Generate switch statements
compare_strncmp	boolean	No	false	Use strncmp for comparisons
hash_function	string	No	None	Hash function to use
compare_function	string	No	None	Comparison function to use
includes	list	No	[]	List of includes to add
other_flags	list	No	[]	Additional gperf command flags

Usage Examples

Basic CPU Profiling

tests:
  - name: "Basic CPU Profiling"
    execution_environment:
      - type: "gperf_cpu"
        output_file: "cpu_profile.out"
    services:
      server:
        name: "http_server"
        implementation:
          name: "nginx"
          type: "iut"

Advanced Profiling with Custom Settings

tests:
  - name: "Advanced CPU Profiling"
    execution_environment:
      - type: "gperf_cpu"
        output_file: "cpu_profile.out"
        language: "C++"
        readonly_tables: true
        includes: ["<string>", "<vector>"]
        other_flags: ["-v", "--debug"]
    services:
      server:
        name: "quic_server"
        implementation:
          name: "picoquic"
          type: "iut"

Extension Points

The GPerf CPU environment plugin can be extended in several ways:

Custom Profiling Metrics

You can extend the plugin to collect additional metrics:

from panther.plugins.environments.execution_environment.gperf_cpu.gperf_cpu import GperfCpuEnvironment

class EnhancedCpuProfiler(GperfCpuEnvironment):
    """Enhanced CPU profiler with additional metrics."""

    def start_monitoring(self):
        """Start CPU profiling with additional metrics."""
        super().start_monitoring()
        # Add custom metric collection

    def get_metrics(self):
        """Return enhanced metrics."""
        base_metrics = super().get_metrics()
        # Add custom metrics
        return enhanced_metrics

Integration with Visualization Tools

The plugin can be extended to automatically generate visualizations:

def generate_visualization(self, profile_file):
    """Generate visualization from profile data."""
    # Implementation using pprof and graphviz
    pass

Testing and Verification

To test the GPerf CPU environment plugin:

1. Unit Tests: Located in /panther/plugins/environments/execution_environment/gperf_cpu/tests/
2. Integration Tests: Run the following test to verify CPU profiling:

python -m pytest tests/integration/test_gperf_cpu_environment.py

1. Manual Verification:
2. Run an experiment with the GPerf CPU environment
3. Verify profile output is generated correctly
4. Analyze the profile with pprof

Troubleshooting

Common Issues and Solutions

Issue	Solution
Missing profile output	Ensure gperftools is installed and properly configured
Permission issues	Check file permissions for output directory
High overhead	Adjust sampling rate or profile only specific operations
Visualization errors	Install graphviz package for proper rendering

Debugging

For more detailed debugging information:

logging:
  level: DEBUG
  format: "%(asctime)s [%(levelname)s] - %(module)s - %(message)s"

Use pprof --text to view a text summary of profile data for quick analysis.