An advanced C++ tool for analyzing the performance of parallel programs, identifying bottlenecks, and suggesting optimizations. This tool assists with advancing GPU architecture work and provides deep insights into parallel programming performance characteristics.
- Real-time Performance Monitoring: Continuous monitoring of CPU, memory, and GPU utilization
- Thread-level Analysis: Individual thread performance tracking and load balancing analysis
- GPU Performance Profiling: CUDA/OpenCL kernel analysis and GPU utilization monitoring
- System Resource Tracking: Comprehensive system-wide resource usage monitoring
- CPU Bottlenecks: Detection of CPU-bound performance limitations
- Memory Bottlenecks: Identification of memory bandwidth and cache efficiency issues
- GPU Bottlenecks: GPU compute and memory bandwidth saturation detection
- Synchronization Issues: Lock contention and thread synchronization overhead analysis
- Load Imbalance: Uneven work distribution across threads/processes
- Communication Overhead: Inter-thread/process communication bottleneck identification
- Algorithm Optimization: Suggestions for reducing computational complexity
- Memory Access Optimization: Cache-friendly memory pattern recommendations
- GPU Optimization: Kernel launch configuration and memory optimization suggestions
- Parallel Programming Best Practices: Thread management and synchronization improvements
- Load Balancing Strategies: Dynamic work distribution recommendations
- Multi-core CPU Analysis: Per-core utilization and efficiency analysis
- GPU Architecture Support: Support for modern GPU architectures (CUDA, OpenCL)
- NUMA Awareness: Non-uniform memory access pattern analysis
- Vectorization Analysis: SIMD instruction utilization assessment
# Clone the repository
git clone https://github.com/riteshr19/Parallel-Programming-Performance-Analyzer.git
cd Parallel-Programming-Performance-Analyzer
# Create build directory
mkdir build && cd build
# Configure and build
cmake ..
make -j$(nproc)# Analyze a running program
./parallel_analyzer ./my_parallel_program
# Analyze a running process by PID
./parallel_analyzer -p 1234
# Extended analysis with GPU profiling
./parallel_analyzer -g -d 30.0 ./my_gpu_program
# Generate detailed report
./parallel_analyzer -o report.txt ./my_programOptions:
-h, --help Show help message
-p, --pid PID Analyze running process with given PID
-d, --duration SECS Analysis duration in seconds (default: 10.0)
-g, --gpu Enable GPU analysis
-v, --verbose Enable verbose output
-o, --output FILE Output detailed report to file
--no-gpu Disable GPU analysis
--sampling-rate RATE Set sampling rate in Hz (default: 100)
The repository includes several example programs demonstrating different performance characteristics:
cd build
make
./examples/cpu_bound_example 8 1000000Demonstrates CPU-intensive parallel computation with optimization opportunities.
./examples/memory_bound_example 2000 2000 4 1Shows memory access pattern impact on performance with cache-friendly vs cache-unfriendly patterns.
./examples/load_imbalance_example 1 8Illustrates load balancing issues and their impact on parallel efficiency.
- Execution Time: Total and per-thread execution times
- CPU Utilization: Per-core and aggregate CPU usage
- Memory Usage: Physical and virtual memory consumption
- GPU Metrics: GPU utilization, memory usage, kernel performance
- Thread Efficiency: Thread utilization and idle time analysis
- Synchronization Overhead: Time spent in locks and barriers
- Communication Overhead: Inter-thread/process communication costs
The tool automatically identifies and prioritizes performance bottlenecks:
- Severity Scoring: 0-100% severity rating for each bottleneck
- Location Information: Specific components or code sections affected
- Impact Analysis: Performance impact assessment
- Root Cause Analysis: Underlying causes of performance issues
Actionable suggestions with:
- Priority Ranking: 1-5 priority levels
- Potential Improvement: Estimated performance gain percentages
- Implementation Steps: Detailed step-by-step optimization guides
- Best Practices: Industry-standard optimization techniques
- Performance Analyzer: Main analysis engine and coordination
- Metrics Collector: System and process-level data collection
- Bottleneck Detector: Pattern recognition and bottleneck identification
- Optimization Engine: Knowledge-based optimization suggestion system
- GPU Analyzer: Specialized GPU performance analysis
- Parallel Insights: Thread and communication pattern analysis
- Linux: Full feature support with detailed system integration
- GPU Support: CUDA and OpenCL profiling capabilities
- Multi-architecture: x86_64, ARM64 support
- System Calls: /proc filesystem monitoring for system metrics
- Performance Counters: Hardware performance counter integration
- GPU APIs: CUDA Runtime API and OpenCL profiling integration
- Thread Profiling: pthread and std::thread monitoring
- Kernel Profiling: Detailed kernel execution analysis
- Occupancy Analysis: GPU resource utilization assessment
- Memory Bandwidth: GPU memory throughput analysis
- Compute Throughput: FLOPS and computational efficiency metrics
- Amdahl's Law Application: Theoretical scalability limits
- Gustafson's Law Analysis: Fixed-time scalability assessment
- Bottleneck Impact: Scalability limiting factor identification
- Cache Efficiency: L1/L2/L3 cache utilization analysis
- Memory Access Patterns: Spatial and temporal locality assessment
- Memory Bandwidth: System memory throughput analysis
- NUMA Effects: Non-uniform memory access impact analysis
#include "performance_analyzer.h"
// Initialize analyzer
perf_analyzer::PerformanceAnalyzer analyzer;
// Configure analysis
analyzer.setAnalysisDuration(30.0);
analyzer.enableGPUAnalysis(true);
// Analyze program
bool success = analyzer.analyzeProgram("./my_program", {"arg1", "arg2"});
// Get results
const auto& metrics = analyzer.getMetrics();
const auto& bottlenecks = analyzer.getBottlenecks();
const auto& suggestions = analyzer.getSuggestions();#include "metrics_collector.h"
perf_analyzer::MetricsCollector collector;
collector.setTargetProcess(pid);
collector.startCollection();
// ... run analysis ...
auto metrics = collector.collectCurrentMetrics();
collector.stopCollection();We welcome contributions! Please see our contributing guidelines:
- Code Style: Follow existing C++ coding conventions
- Testing: Add tests for new features
- Documentation: Update documentation for API changes
- Performance: Ensure minimal overhead from analysis itself
# Install development dependencies
sudo apt-get install cmake build-essential
# Optional: GPU development
sudo apt-get install nvidia-cuda-dev opencl-headers
# Build with debug info
mkdir debug && cd debug
cmake -DCMAKE_BUILD_TYPE=Debug ..
make -j$(nproc)This tool was developed to advance GPU architecture research and parallel programming optimization. It provides:
- Quantitative Analysis: Precise performance measurements and bottleneck quantification
- Comparative Studies: Before/after optimization impact assessment
- Architecture Insights: Deep understanding of hardware-software interaction
- Scalability Research: Theoretical and practical scalability analysis
If you use this tool in academic research, please cite:
Parallel Programming Performance Analyzer
Advanced tool for parallel program analysis and optimization
GitHub: https://github.com/riteshr19/Parallel-Programming-Performance-Analyzer
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
For questions, issues, or feature requests:
- Issues: Use GitHub Issues for bug reports and feature requests
- Discussions: Use GitHub Discussions for questions and community support
- Documentation: Check the docs/ directory for detailed documentation
Advancing GPU Architecture and Parallel Programming Performance 🚀