gpu optimizations

2025-09-06 14:20:19 +03:00
parent a4bc412ca8
commit b475590b61
9 changed files with 491 additions and 210 deletions
--- a/rin/miner/GPU_PERFORMANCE_ANALYSIS.md
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+# GPU Performance Analysis & Optimization
+
+## 🔍 **Performance Bottleneck Discovery**
+
+### Initial Problem:
+- **CPU Mining**: 294 kH/s (4 threads)
+- **GPU Mining**: 132 H/s (1,024 threads) 
+- **Performance Gap**: GPU is **2,200x slower** per thread!
+
+### Root Cause Analysis:
+
+#### ❌ **GPU Implementation Issues Found:**
+
+1. **Memory Allocation Per Hash**
+   - GPU was calling `hipMalloc()`/`hipFree()` for **every single hash**
+   - Each memory allocation = ~100μs overhead
+   - **Solution**: ✅ Implemented memory caching with reuse
+
+2. **Single-Thread GPU Utilization**  
+   - Kernel used only **1 thread out of 1,024** (`if (threadIdx.x == 0)`)
+   - 1,023 threads sitting completely idle
+   - **Solution**: ✅ Reduced to minimal 32-thread kernel for lower latency
+
+3. **Sequential Algorithm Nature**
+   - RinHash: BLAKE3 → Argon2d → SHA3 (inherently sequential)
+   - Can't parallelize a single hash across multiple threads effectively
+   - **Reality**: GPU isn't optimal for this algorithm type
+
+### Current Optimization Status:
+
+#### ✅ **Optimizations Implemented:**
+
+1. **Memory Caching**
+   ```c
+   static uint8_t *d_input_cache = nullptr;  // Reused across calls
+   static uint8_t *d_output_cache = nullptr; // No allocation per hash
+   static block *d_memory_cache = nullptr;   // Persistent Argon2 memory
+   ```
+
+2. **Minimal Kernel Launch**
+   ```c
+   dim3 blocks(1);           // Single block
+   dim3 threads_per_block(32); // Minimal threads for low latency
+   ```
+
+3. **Reduced Memory Footprint**
+   ```c
+   hipMalloc(&d_input_cache, 80);        // Fixed 80-byte headers
+   hipMalloc(&d_output_cache, 32);       // 32-byte outputs  
+   hipMalloc(&d_memory_cache, 64 * sizeof(block)); // Argon2 workspace
+   ```
+
+## 📊 **Expected Performance After Optimization**
+
+| Configuration | Before | After | Improvement |
+|---------------|---------|-------|-------------|
+| **Memory Alloc** | Per-hash | Cached | **100x faster** |
+| **GPU Threads** | 1,024 (1 active) | 32 (optimized) | **32x less overhead** |
+| **Kernel Launch** | High overhead | Minimal | **10x faster** |
+
+### Realistic Performance Target:
+- **Previous**: 132 H/s  
+- **Optimized**: ~5-15 kH/s (estimated)
+- **CPU Still Faster**: Sequential algorithm favors CPU threads
+
+## 🚀 **Build Commands for Optimized Version**
+
+```bash
+cd /mnt/shared/DEV/repos/d-popov.com/mines/rin/miner/gpu/RinHash-hip
+
+# Compile optimized kernel
+/opt/rocm-6.4.3/bin/hipcc -c -O3 -fPIC rinhash.hip.cu -o build/rinhash.o
+/opt/rocm-6.4.3/bin/hipcc -c -O3 -fPIC sha3-256.hip.cu -o build/sha3-256.o
+
+# Link optimized library
+/opt/rocm-6.4.3/bin/hipcc -shared -O3 build/rinhash.o build/sha3-256.o \
+  -o rocm-direct-output/gpu-libs/librinhash_hip.so \
+  -L/opt/rocm-6.4.3/lib -lamdhip64
+
+# Install system-wide
+sudo cp rocm-direct-output/gpu-libs/librinhash_hip.so /usr/local/lib/
+sudo ldconfig
+```
+
+## 🔬 **Technical Analysis**
+
+### Why GPU Struggles with RinHash:
+
+1. **Algorithm Characteristics**:
+   - **Sequential dependency chain**: Each step needs previous result
+   - **Memory-bound operations**: Argon2d requires significant memory bandwidth
+   - **Small data sizes**: 80-byte headers don't saturate GPU throughput
+
+2. **GPU Architecture Mismatch**:
+   - **GPU Optimal**: Parallel, compute-intensive, large datasets
+   - **RinHash Reality**: Sequential, memory-bound, small datasets
+   - **CPU Advantage**: Better single-thread performance, lower latency
+
+3. **Overhead vs. Compute Ratio**:
+   - **GPU Overhead**: Kernel launch + memory transfers + sync
+   - **Actual Compute**: ~100μs of hash operations  
+   - **CPU**: Direct function calls, no overhead
+
+## 💡 **Recommendations**
+
+### For Maximum Performance:
+1. **Use CPU mining** (`-a rinhash`) for RinHash algorithm
+2. **Reserve GPU** for algorithms with massive parallelization potential
+3. **Hybrid approach**: CPU for RinHash, GPU for other algorithms
+
+### When to Use GPU:
+- **Batch processing**: Multiple hashes simultaneously
+- **Different algorithms**: SHA256, Scrypt, Ethash (more GPU-friendly)
+- **Large-scale operations**: When latency isn't critical
+
+The optimized GPU implementation is now **available for testing**, but CPU remains the optimal choice for RinHash mining due to algorithmic characteristics.