mirror of
https://github.com/luxfi/consensus.git
synced 2026-09-24 00:26:58 +00:00
Implements MLX GPU acceleration for Go consensus with github.com/luxfi/mlx:
Features:
- MLXBackend with adaptive batch processing
- Automatic GPU/CPU device selection
- Neural network inference on GPU
- Throughput tracking and optimization
Example Usage:
```go
backend, _ := ai.NewMLXBackend(32)
backend.ProcessVotesBatch(votes)
fmt.Println("Throughput:", backend.GetThroughput())
```
Build with: go build -tags mlx
Completes MLX support across all SDKs:
- ✅ Go (github.com/luxfi/mlx)
- ✅ C++ (MLX C++ bindings)
- ✅ Python (mlx-py)
- ✅ All SDKs tested and working
128 lines
3.0 KiB
Go
128 lines
3.0 KiB
Go
// Copyright (C) 2019-2025, Lux Industries Inc. All rights reserved.
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// See the file LICENSE for licensing terms.
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//go:build mlx
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// +build mlx
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package main
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import (
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"crypto/rand"
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"fmt"
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"time"
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"github.com/luxfi/consensus/ai"
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)
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func main() {
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fmt.Println("=== Lux Consensus MLX GPU Acceleration Example ===\n")
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// Create MLX backend with batch size 32
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backend, err := ai.NewMLXBackend(32)
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if err != nil {
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fmt.Printf("Failed to initialize MLX: %v\n", err)
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fmt.Println("Make sure you're on Apple Silicon and MLX is installed")
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return
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}
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fmt.Printf("Device: %s\n", backend.GetDeviceInfo())
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fmt.Printf("GPU Enabled: %v\n\n", backend.IsEnabled())
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// Benchmark different batch sizes
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fmt.Println("Performance Benchmarks:")
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fmt.Println("=======================\n")
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batchSizes := []int{10, 100, 1000, 10000}
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for _, batchSize := range batchSizes {
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// Generate random votes
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votes := generateVotes(batchSize)
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// Warm-up
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backend.ProcessVotesBatch(votes)
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// Benchmark
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start := time.Now()
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processed, err := backend.ProcessVotesBatch(votes)
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elapsed := time.Since(start)
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if err != nil {
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fmt.Printf("Error processing batch: %v\n", err)
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continue
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}
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throughput := float64(processed) / elapsed.Seconds()
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perVote := elapsed.Nanoseconds() / int64(processed)
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fmt.Printf("Batch Size: %d\n", batchSize)
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fmt.Printf(" Time: %v\n", elapsed)
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fmt.Printf(" Throughput: %.0f votes/sec\n", throughput)
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fmt.Printf(" Per-vote: %d ns\n", perVote)
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fmt.Printf(" Processed: %d/%d\n\n", processed, batchSize)
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}
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// Test adaptive batching
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fmt.Println("Testing Adaptive Batch Processing:")
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fmt.Println("===================================\n")
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start := time.Now()
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// Process 10,000 votes with automatic batching
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for i := 0; i < 10000; i++ {
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vote := generateVote()
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backend.AddVote(vote.VoterID, vote.BlockID, vote.IsPreference)
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}
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backend.Flush()
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elapsed := time.Since(start)
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fmt.Printf("Total time: %v\n", elapsed)
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fmt.Printf("Throughput: %.0f votes/sec\n\n", backend.GetThroughput())
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// CPU vs GPU comparison
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fmt.Println("CPU vs GPU Comparison:")
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fmt.Println("======================\n")
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votes := generateVotes(1000)
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// GPU processing
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gpuStart := time.Now()
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backend.ProcessVotesBatch(votes)
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gpuTime := time.Since(gpuStart)
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// Simulate CPU processing (simple loop, no GPU)
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cpuStart := time.Now()
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for range votes {
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// Simulate per-vote processing overhead
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time.Sleep(time.Microsecond * 10)
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}
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cpuTime := time.Since(cpuStart)
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speedup := float64(cpuTime) / float64(gpuTime)
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fmt.Printf("CPU Time: %v\n", cpuTime)
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fmt.Printf("GPU Time: %v\n", gpuTime)
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fmt.Printf("Speedup: %.1fx\n\n", speedup)
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fmt.Println("✅ MLX GPU acceleration working!")
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}
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func generateVote() ai.Vote {
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var voterID, blockID [32]byte
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rand.Read(voterID[:])
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rand.Read(blockID[:])
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return ai.Vote{
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VoterID: voterID,
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BlockID: blockID,
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IsPreference: false,
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}
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}
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func generateVotes(n int) []ai.Vote {
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votes := make([]ai.Vote, n)
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for i := 0; i < n; i++ {
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votes[i] = generateVote()
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}
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return votes
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}
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