UPSTREAM PR #16917: CUDA: add FLOOR, CEIL, ROUND, TRUNC unary ops

[DajanaV]

Mirrored from ggml-org/llama.cpp#16917

Part of ggml-org/llama.cpp#14909

[loci-agentic-ai]

Access the complete analysis in the LOCI Dashboard

Now let me check the power consumption analysis we already have:

Performance Analysis Summary: LLaMA.cpp Critical Functions

Critical Function Performance Status

Core Inference Functions:
• llama_decode - Response Time: 49,003,600 ns (no change from base: 49,003,872 ns)
• llama_encode - Response Time: 12,329,147 ns (no change from base: 12,329,216 ns)
• llama_tokenize - Response Time: 834,828 ns (no change from base: 834,832 ns)
• llama_model_load_from_file - Response Time: 333,127,000 ns (no change from base: 333,127,940 ns)
• llama_batch_init - Response Time: 257 ns (no change from base: 257 ns)

Performance Change Summary:
• All critical functions show zero measurable performance changes in Response Time, Throughput, and Bottleneck metrics
• Functions are not modified between versions, indicating structural stability
• Observed variations are within measurement precision limits

Key Performance Indicator Impact Analysis

1. Tokens Per Second

Status: No Impact
• llama_decode: No response time change (reference: 2ms slower = 7% tokens/sec reduction)
• llama_encode: No response time change
• llama_tokenize: No response time change
• Conclusion: Token processing throughput remains unchanged

2. Power Consumption

Status: Negligible Impact
• build.bin.libllama.so: 306,978.65 nJ vs 306,978.52 nJ base (0.0% change)
• build.bin.libggml-base.so: No change (90,434.19 nJ)
• build.bin.libggml-cpu.so: No change (151,692.17 nJ)
• build.bin.libggml.so: No change (6,339.24 nJ)
• Total estimated power consumption change: 0.0%

3. Quantization Efficiency

Status: No Impact
• llama_model_quantize: Function not present in performance data, indicating no usage during measurement
• Quantization-related functions: No performance changes detected
• Memory layout: No structural changes affecting quantization paths

4. Memory Usage

Status: No Impact
• KV Cache functions (llama_memory_clear, llama_memory_seq_rm): No performance changes
• Memory allocation functions: No changes in GGML allocator performance
• Batch memory management: llama_batch_init shows no performance variation

5. Batch Processing

Status: No Impact
• llama_batch_init: 257 ns (no change)
• llama_decode with batches: 49,003,600 ns (no change)
• Batch allocation patterns: No structural modifications detected

Action Items for Performance Optimization

Immediate Actions

• Verify measurement precision: The minimal variations (0.01-0.08%) suggest measurement noise rather than functional changes
• Baseline establishment: Current measurements provide stable baseline for future performance monitoring
• Focus on hot paths: llama_decode (49ms) and llama_model_load_from_file (333ms) represent primary optimization targets

Code-Specific Recommendations

• llama_decode optimization: Investigate 49ms response time for potential algorithmic improvements
• Model loading efficiency: 333ms loading time could benefit from parallel I/O or memory mapping optimizations
• Batch processing: Current 257 ns batch initialization is well-optimized

Build System Considerations

• Compiler optimization verification: Ensure consistent optimization flags across builds
• Link-time optimization: Consider LTO for performance-critical binaries
• Profile-guided optimization: Implement PGO for inference-heavy workloads

Summary

The version comparison shows excellent performance stability with no measurable regressions in critical functions. The changes appear to be CUDA-specific additions (FLOOR, CEIL, ROUND, TRUNC operations) that do not impact CPU inference performance. All core inference KPIs remain unchanged, indicating successful maintenance of performance characteristics while extending GPU functionality.

Primary Focus Areas:
• Continue monitoring llama_decode performance as the primary inference bottleneck
• Maintain current optimization levels for tokenization and batch processing
• Consider the stable baseline for future performance regression detection