Performance Evaluation

Performance Evaluation in LLMIR

LLMIR includes comprehensive benchmarking and evaluation methodologies to measure its impact on LLM inference performance across different models and hardware platforms.

Benchmark Framework ¶

LLMIR will provide a dedicated benchmarking framework to evaluate performance improvements:

// LLMIR Benchmark API (Planned)
class LLMIRBenchmark {
public:
  // Configure benchmark parameters
  void setModel(const std::string& modelPath);
  void setHardware(const std::string& hardware);
  void setSequenceLength(int length);
  void setBatchSize(int batchSize);
  void setQuantizationMode(QuantMode quantMode);
  void setKVCacheStrategy(KVCacheMode kvMode);
  
  // Run benchmarks
  BenchmarkResult runThroughputTest(int iterations);
  BenchmarkResult runLatencyTest(int iterations);
  BenchmarkResult runMemoryTest();
  
  // Compare with baselines
  ComparisonResult compareWithBaseline(const std::string& baselineFramework);
};

Key Performance Metrics ¶

LLMIR will track and optimize for several key performance metrics:

Throughput Metrics ¶

• Tokens per Second (TPS): Number of output tokens generated per second
• Requests per Second (RPS): Number of inference requests processed per second
• Effective TPS: Combined throughput across multiple devices/nodes

Latency Metrics ¶

• First Token Latency: Time from request reception to first token generation
• Inter-Token Latency: Time between consecutive token generations
• End-to-End Latency: Total time from request to completion
• Attention Computation Latency: Time spent in attention operations
• KV Cache Access Latency: Time spent accessing the KV cache

Memory Metrics ¶

• Peak Memory Usage: Maximum memory consumed during inference
• Memory Efficiency: Ratio of active tensors to allocated memory
• KV Cache Size: Memory consumed by the key-value cache
• Memory Bandwidth Utilization: Efficiency of memory access patterns

Scaling Metrics ¶

• Strong Scaling: Speedup when increasing devices for fixed workload
• Weak Scaling: Performance with fixed workload per device while increasing devices
• Device Utilization: Percentage of device compute capacity used

Benchmark Suite ¶

LLMIR will include a comprehensive benchmark suite with:

Model Selection ¶

• Size Variants: Small (7B), Medium (13B), Large (70B+)
• Architecture Types: Decoder-only, Encoder-decoder
• Model Families: Llama, Mistral, Falcon, etc.

Workload Patterns ¶

• Text Generation: Standard autoregressive generation
• Chat Completion: Multi-turn dialogue generation
• Long Context Processing: Tests with very long input contexts
• Mixed Batch Sizes: Varying concurrent request volumes

Hardware Targets ¶

• NVIDIA GPUs: A100, H100, RTX series
• AMD GPUs: MI100, MI250, MI300
• x86 CPUs: Intel Xeon, AMD EPYC
• ARM CPUs: AWS Graviton, Apple Silicon

Analysis Tools ¶

LLMIR will provide tools for detailed performance analysis:

Profiling ¶

// LLMIR Profiler API (Planned)
class LLMIRProfiler {
public:
  // Start/stop profiling
  void startProfiling(const std::string& name);
  void stopProfiling();
  
  // Event tracking
  void recordEvent(const std::string& name);
  void markOperationStart(const std::string& opName);
  void markOperationEnd(const std::string& opName);
  
  // Analysis
  ProfileData getOperationBreakdown();
  ProfileData getMemoryUsageTimeline();
  ProfileData getDeviceUtilization();
  
  // Export
  void exportChromeTraceFormat(const std::string& filename);
  void exportReport(const std::string& filename);
};

Visualization ¶

The benchmarking system will include visualizations to help understand performance:

• Operation timeline views
• Memory usage graphs
• Compute/memory utilization heatmaps
• Performance comparison charts

Baseline Comparisons ¶

LLMIR performance will be compared against several baselines:

• vLLM Native: Performance compared to unmodified vLLM
• SGLang Native: Performance compared to unmodified SGLang
• HuggingFace Transformers: Performance relative to standard implementations
• TensorRT-LLM: Comparison with NVIDIA’s optimized framework
• Native Hardware Libraries: Comparison with vendor-specific implementations

Future Directions ¶

As LLMIR matures, the performance evaluation framework will expand to include:

• Automated Regression Testing: Continuous performance monitoring
• Bottleneck Identification: Automatic detection of performance limitations
• Optimization Recommendation: Suggestions for performance improvements
• Hardware-Specific Insights: Targeted optimizations based on profiling
• Performance Modeling: Predictive modeling of optimization impacts

This performance evaluation system is under development as part of the LLMIR project.