Benchmark whichmut dense vs sparse formats for re-implementation decision

[jgallowa07]

Final Results: GPU Benchmarking Complete - Dense is 2.3x Faster!

Status: ✅ COMPLETE - CPU and GPU benchmarking finished with surprising results!

Major Finding: Dense format is 2.3x FASTER than sparse on GPU at production scale, completely reversing the initial expectations.

Recommendation: Use DENSE format for production GPU training.

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Executive Summary

What We Discovered

The original 6,600x performance gap was not due to the data format but rather inefficient Python loops. After implementing vectorized dense:

CPU Results:

• Sparse: 0.070s/epoch
• Dense: 0.084s/epoch (+20% slower)
• Both formats viable

GPU Results (Game Changer!):

• Small dataset (225 seqs): Nearly identical performance
• Large dataset (64k seqs): Dense is 2.3x FASTER than sparse!
• Dense: 24.1s/epoch vs Sparse: 54.9s/epoch

Final Recommendation

For Production (GPU Training):
✅ Use DENSE format

• 2.3x faster on GPU with large datasets
• Simpler code, easier to debug
• Better GPU utilization through simpler indexing
• Minimal memory overhead (~9%)

For CPU-Only Workflows:

• Use SPARSE format (20% faster than dense)

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Complete Results

Experiment Summary

Experiment	Device	Dataset	Batch Size	N Seqs	Sparse Time/Epoch	Dense Time/Epoch	Ratio	Notes
cpu_test_bs32	CPU	225	32	180/45	0.070s	0.084s	1.20x slower	CPU baseline
gpu_test_bs32	GPU	225	32	180/45	0.127s	0.132s	1.04x slower	GPU small
gpu_21K_bs128	GPU	64,465	128	64,465	54.95s	24.10s	2.28x FASTER	Production scale

Key Findings

1. Bug Fix: Fixed dense tensor shape from (L_codon, 65, 65) to (L_codon, 65)

   • Branch: viral-dasm-experiments-1/fix-dense-neutral-rates-tensor-shape
   • PR: matsengrp/viral-dasm-experiments-1#42

2. Vectorization: Eliminated Python loops, achieved 5,785x speedup

   • Old dense (iterative): 462.8s/epoch (6,600x slower)
   • New dense (vectorized): 0.084s/epoch on CPU, 24.1s/epoch on GPU
   • Branch: 171-vectorized-dense-implementation (netam)

3. Mathematical Equivalence: Both formats produce identical results

   • Same validation losses
   • Same model parameters (diff < 1e-8)
   • Same predictions (diff < 1e-8)

4. GPU Performance Reversal: Dense is significantly faster on GPU

   • Simpler indexing patterns → better GPU parallelization
   • Coalesced memory access patterns
   • Scales better with dataset size and batch size

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Why Dense is Faster on GPU

Technical Explanation:

1. Simpler Indexing: Dense uses straightforward tensor indexing, sparse requires complex lookup operations
2. Better GPU Utilization: Dense operations are more amenable to GPU parallelization
3. Coalesced Memory Access: Dense format enables better memory coalescing on GPU
4. Scales Better: Performance advantage grows with dataset size and batch size

Vectorized Dense Implementation:

# Build lookup table once (65 codons, not millions of data points)
child_indices = parent_to_children[codon_parents_idxss]  # (N, L, 9)

# Parallel tensor operations - NO PYTHON LOOPS
neutral_rates_gathered = neutral_rates_tensor[batch_idx, pos_idx, child_indices]
selection_factors_gathered = selection_factors[batch_idx, pos_idx, child_aa_idx]

# Vectorized multiply and sum
products = neutral_rates_gathered * selection_factors_gathered
Z = masked_products.sum(dim=(1, 2))

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Implementation Details

Changes Made

netam Repository:

• Branch: 171-vectorized-dense-implementation
• File: netam/whichmut_trainer.py:337-429
• Status: All 27 whichmut tests pass
• Created vectorized dense implementation alongside existing sparse

viral-dasm-experiments-1 Repository:

• Branch: 171-benchmark-sparse-v-dense-whichmut (experimental, not for merging)
• Branch: fix-dense-neutral-rates-tensor-shape (bug fix, PR add ability to see attention map #42)
• Bug fix: src/viraldasmex/dasm_utils.py:232
• Tests: tests/test_dasm_utils.py:281
• Comprehensive benchmarking infrastructure

Benchmarking Infrastructure

Created reusable infrastructure in viral-dasm-experiments-1/scv2-dasm/benchmark_sparse_v_dense/:

• Parameterized experiment runner
• Memory and timing tracking
• Model comparison verification
• Organized experiment results

Complete Documentation:
📊 Full Results & Analysis

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Decision Framework

Use DENSE if:

✅ Training on GPU (2.3x faster at scale!) - RECOMMENDED
✅ Code simplicity is a priority
✅ Debugging and understanding code is important
✅ Using large batch sizes (>64)
✅ Production training with thousands of sequences
✅ New implementation or fresh codebase

Use SPARSE if:

• Training on CPU only (20% faster than dense)
• GPU memory is severely constrained
• Already using sparse format in existing codebase
• Team has significant investment in sparse data structures

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Experiment Results

CPU Baseline (225 sequences, batch 32)

• Device: CPU (MPS not compatible)
• Sparse: 0.070s/epoch, 66.3 MB memory
• Dense: 0.084s/epoch, 66.1 MB memory
• Outcome: Sparse 20% faster on CPU

GPU Small Dataset (225 sequences, batch 32)

• Device: CUDA (orca04)
• Sparse: 0.127s/epoch, 48 MB GPU memory
• Dense: 0.132s/epoch, 60 MB GPU memory
• Outcome: Nearly identical (~4% difference)

GPU Production Scale (64,465 sequences, batch 128)

• Device: CUDA (ermine)
• Sparse: 54.95s/epoch, 194 MB GPU memory
• Dense: 24.10s/epoch, 212 MB GPU memory
• Outcome: Dense 2.28x FASTER than sparse!

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What Changed from Original Plan

Original Plan (Not Executed)

• ❌ Synthetic data generation
• ❌ On-the-fly neutral rate computation (infrastructure doesn't exist)
• ❌ 10,000 sequences benchmark (used real datasets: 225 for validation, 64k for production)

Actual Approach (What We Did)

1. Used Empirical Data: SARS-CoV-2 spike sequences from existing pipeline
2. Found Critical Bug: Dense tensor shape was incorrect
3. Discovered Root Cause: 6,600x slowdown from Python loops, not data format
4. Implemented Vectorized Dense: Achieved 5,785x speedup
5. Verified Equivalence: Both implementations mathematically identical
6. GPU Benchmarking: Discovered dense is 2.3x faster on GPU!

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Memory Analysis

CPU

• Dense and sparse have similar memory footprints for small datasets
• Difference becomes more apparent at scale

GPU (Production Scale - 64k sequences)

• Sparse: 194 MB GPU memory reserved
• Dense: 212 MB GPU memory reserved
• Difference: ~9% more for dense (minimal and acceptable)

Both formats fit comfortably in GPU memory for typical workloads. Dense's 2.3x speed advantage far outweighs the 9% memory increase.

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Timeline

• Day 1-2: Initial benchmarking, discovered 6,600x slowdown
• Day 3: Root cause analysis, identified Python loops
• Day 4: Implemented vectorized dense, achieved 5,785x speedup
• Day 5: Verified equivalence, reorganized infrastructure
• Day 6-7: GPU benchmarking, discovered dense is 2.3x faster!

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Key Documentation

Main Results

📊 SUMMARY.md - Complete Results & Analysis

Supporting Documentation

• Performance Analysis - Root cause of Python loop bottleneck
• Vectorization Results - Detailed vectorization improvements
• Benchmark Summary - Initial findings
• README - Benchmarking methodology

Experiment Data

All experiment results available in:
viral-dasm-experiments-1/scv2-dasm/benchmark_sparse_v_dense/experiments/

• 2025-11-05_cpu_test_bs32/ - CPU baseline
• 2025-11-05_gpu_test_bs32/ - GPU small dataset
• 2025-11-06_gpu_21K_bs128/ - GPU production scale

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Related PRs and Branches

netam

• Branch: 171-vectorized-dense-implementation
• File: netam/whichmut_trainer.py:337-429
• Tests: All 27 whichmut tests pass

viral-dasm-experiments-1

• Bug Fix Branch: fix-dense-neutral-rates-tensor-shape
• Bug Fix PR: add ability to see attention map #42 (ready for merge)
• Benchmarking Branch: 171-benchmark-sparse-v-dense-whichmut (experimental, not for merging)

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Next Steps

1. ✅ CPU validation complete
2. ✅ GPU benchmarks complete
3. ✅ Production-scale testing (64k sequences) complete
4. ✅ Discovered dense is 2.3x faster on GPU!
5. ✅ Merge vectorized dense implementation to netam main
6. ⏳ Update netam documentation with GPU-based recommendations
7. ⏳ Consider deprecating iterative dense implementation
8. ⏳ Update whichmut documentation to recommend dense for GPU training

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Conclusion

This investigation revealed a fundamental performance reversal based on hardware:

Before Investigation

• Dense was 6,600x slower (unusable due to Python loops)
• Sparse was the only option

After Vectorization

• CPU: Sparse is 20% faster (modest advantage)
• GPU: Dense is 2.3x faster (major advantage!)

Final Recommendation:

• ✅ Production GPU training: Use DENSE (faster + simpler)
• ✅ CPU-only workflows: Use SPARSE (faster on CPU)

The performance winner depends on the hardware. Dense's simpler indexing patterns that are neutral on CPU become a major advantage on GPU, delivering 2.3x speedup at production scale while also being conceptually simpler and easier to maintain.

For re-implementation: Use DENSE format for GPU-based production training.