UPSTREAM PR #17305: metal : add cumsum

[DajanaV]

Mirrored from ggml-org/llama.cpp#17305

cont #17063

2-pass prefix sum implementation

[loci-review]

Access the complete analysis in the LOCI Dashboard

Performance Analysis Summary

Overview

The analysis examined PR #227 implementing Metal GPU acceleration for cumulative sum (CUMSUM) operations in LLaMA.cpp. This adds a 2-pass prefix sum algorithm with SIMD optimizations for Apple Silicon GPUs.

Performance Impact Assessment

Condition 1 applies: The performance changes are negligible in absolute terms despite appearing significant in percentages.

The highest percentage changes identified were:

• Response Time: llm_graph_input_out_ids::can_reuse() improved by 0.096% (0.06 ns absolute change)
• Throughput Time: std::_Optional_base constructor degraded by 0.171% (0.04 ns absolute change)

These sub-nanosecond variations fall well within measurement noise and represent normal compiler optimization differences rather than meaningful performance changes.

Core Function Impact

No core inference functions were affected. The changes do not impact critical performance paths:

• llama_decode(), llama_encode(), llama_tokenize() - No changes detected
• Model loading, memory management, batch processing - Unaffected

Tokens per second impact: Zero. Since no tokenization or inference functions show meaningful performance changes, there will be no impact on inference throughput.

Power Consumption Analysis

All binaries maintain stable energy efficiency:

• libllama.so, llama-run, llama-tts show negligible power consumption variations (<0.001%)
• No energy efficiency regressions identified across the 16 analyzed binaries

Technical Implementation

Flame Graph Analysis: Confirmed llm_graph_input_out_ids::can_reuse() operates as a simple leaf function with no external dependencies, validating the minimal performance impact.

CFG Comparison: Revealed identical assembly code between versions, confirming that timing differences result from external factors (cache alignment, micro-architectural variations) rather than functional changes.

Code Review Findings: The CUMSUM implementation demonstrates solid engineering practices with proper memory management, comprehensive testing, and maintains backward compatibility. No critical issues identified.

Conclusion

This PR successfully adds Metal GPU acceleration for cumulative sum operations without affecting existing performance. The implementation enhances computational capabilities while maintaining system stability and efficiency. The observed performance variations are statistically insignificant and do not warrant concern.