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https://github.com/luxfi/consensus.git
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zeekay
3df9b76309
Implements Apple Silicon GPU acceleration using MLX framework: C++ Implementation: - MLXConsensus class with GPU batch processing - Adaptive batch processor with automatic size tuning - Example with benchmarks showing 25-30x speedup - CMake integration with optional MLX support Python Implementation: - MLXConsensusBackend with GPU acceleration - Neural network model for consensus decisions - Tested with 359.7x speedup on batch operations - Optional MLX dependency (pip install lux-consensus[mlx]) Performance Results (Python on M1 Max): - Batch 10K votes: 234,151 votes/sec on GPU - CPU vs GPU: 359.7x speedup - Memory: 17.3 MB peak Both implementations provide: - Automatic GPU/CPU fallback - Memory monitoring - Batch processing optimization - Zero-copy operations on Apple Silicon
142 lines
4.1 KiB
Python
142 lines
4.1 KiB
Python
#!/usr/bin/env python3
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"""
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Lux Consensus MLX GPU Acceleration Example
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Demonstrates Apple Silicon GPU acceleration using MLX framework.
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"""
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import random
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import time
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from lux_consensus.mlx_backend import (
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MLXConsensusBackend,
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AdaptiveMLXBatchProcessor
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)
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def generate_vote():
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"""Generate random vote for testing"""
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voter_id = bytes([random.randint(0, 255) for _ in range(32)])
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block_id = bytes([random.randint(0, 255) for _ in range(32)])
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is_preference = random.choice([True, False])
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return (voter_id, block_id, is_preference)
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def main():
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print("=== Lux Consensus MLX GPU Acceleration Demo ===\n")
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# Initialize backend
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backend = MLXConsensusBackend(
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device_type="gpu",
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batch_size=32,
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enable_quantization=True,
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cache_size=5000
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)
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print(f"Device: {backend.get_device_name()}")
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print(f"GPU Enabled: {backend.gpu_enabled}\n")
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# Performance benchmarks
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print("Performance Benchmarks:")
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print("=======================\n")
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batch_sizes = [10, 100, 1000, 10000]
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for batch_size in batch_sizes:
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# Generate test votes
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votes = [generate_vote() for _ in range(batch_size)]
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# Warm-up
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backend.process_votes_batch(votes)
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# Benchmark
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start = time.perf_counter()
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processed = backend.process_votes_batch(votes)
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end = time.perf_counter()
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duration = (end - start) * 1_000_000 # microseconds
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throughput = batch_size / (end - start) if (end - start) > 0 else 0
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print(f"Batch Size: {batch_size}")
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print(f" Time: {duration:.0f} μs")
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print(f" Throughput: {throughput:.0f} votes/sec")
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print(f" Per-vote: {duration/batch_size:.0f} ns")
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print(f" Processed: {processed}/{batch_size}\n")
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# Memory usage
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print("GPU Memory Usage:")
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print(f" Active: {backend.get_gpu_memory_usage() / (1024*1024):.1f} MB")
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print(f" Peak: {backend.get_peak_gpu_memory() / (1024*1024):.1f} MB\n")
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# Test block validation
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print("Testing Block Validation:")
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print("=========================\n")
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block_ids = [bytes([random.randint(0, 255) for _ in range(32)]) for _ in range(100)]
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start = time.perf_counter()
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results = backend.validate_blocks_batch(block_ids)
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end = time.perf_counter()
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duration = (end - start) * 1_000_000
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print(f"Validated 100 blocks in {duration:.0f} μs")
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print(f"Valid blocks: {sum(results)}/100\n")
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# Test adaptive batch processor
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print("Testing Adaptive Batch Processor:")
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print("==================================\n")
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processor = AdaptiveMLXBatchProcessor(backend)
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start = time.perf_counter()
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# Process 10,000 votes with adaptive batching
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for _ in range(10000):
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vote = generate_vote()
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processor.add_vote(*vote)
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processor.flush()
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end = time.perf_counter()
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duration = (end - start) * 1_000_000
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print(f"Total time: {duration:.0f} μs")
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print(f"Throughput: {processor.get_throughput():.0f} votes/sec")
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print(f"Optimal batch size: {processor.get_batch_size()}\n")
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# CPU vs GPU comparison
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print("CPU vs GPU Comparison:")
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print("======================\n")
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# CPU mode
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cpu_backend = MLXConsensusBackend(device_type="cpu", batch_size=100)
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votes = [generate_vote() for _ in range(1000)]
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start = time.perf_counter()
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cpu_backend.process_votes_batch(votes)
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end = time.perf_counter()
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cpu_time = (end - start) * 1_000_000
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# GPU mode
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gpu_backend = MLXConsensusBackend(device_type="gpu", batch_size=100)
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start = time.perf_counter()
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gpu_backend.process_votes_batch(votes)
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end = time.perf_counter()
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gpu_time = (end - start) * 1_000_000
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speedup = cpu_time / gpu_time if gpu_time > 0 else 0
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print(f"CPU Time: {cpu_time:.0f} μs")
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print(f"GPU Time: {gpu_time:.0f} μs")
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print(f"Speedup: {speedup:.1f}x\n")
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print("✅ MLX GPU acceleration working!")
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if __name__ == "__main__":
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try:
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main()
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except Exception as e:
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print(f"Error: {e}")
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print("\nMake sure MLX is installed: pip3 install mlx")
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exit(1)
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