NN clusterizer: Improve filling kernel speed

GPU/GPUTracking/Global/GPUChainTrackingClusterizer.cxx

@@ -706,6 +706,8 @@ int32_t GPUChainTracking::RunTPCClusterizer(bool synchronizeOutput)
         nnApplications[lane].initClusterizer(nn_settings, clustererNNShadow);
       }
       AllocateRegisteredMemory(clustererNN.mMemoryId);
+      // nnApplications[lane].createBoundary(clustererNNShadow);
+      // nnApplications[lane].createIndexLookup(clustererNNShadow);
     });
     if (doGPU) {
       WriteToConstantMemory(RecoStep::TPCClusterFinding, (char*)&processors()->tpcNNClusterer - (char*)processors(), &processorsShadow()->tpcNNClusterer, sizeof(GPUTPCNNClusterizer) * NSECTORS, mRec->NStreams() - 1, &mEvents->init);

GPU/GPUTracking/TPCClusterFinder/GPUTPCNNClusterizer.cxx

@@ -65,33 +65,6 @@ void* GPUTPCNNClusterizer::setIOPointers(void* mem)
   return mem;
 }
 
-// std::vector<int32_t> GPUTPCNNClusterizer::pointerSizes() {
-//   std::vector<int32_t> sizes(7, -1);
-//   if (mNnClusterizerBatchedMode > 0) {
-//     if (mNnInferenceInputDType == 0 && mNnClusterizerElementSize > 0) {
-//       sizes[0] = mNnClusterizerBatchedMode * mNnClusterizerElementSize; // inputData16
-//     } else if (mNnInferenceInputDType == 1 && mNnClusterizerElementSize > 0) {
-//       sizes[1] = mNnClusterizerBatchedMode * mNnClusterizerElementSize; // inputData32
-//     }
-//     sizes[2] = 2 * mNnClusterizerBatchedMode; // mClusterFlags
-//     if (mNnClusterizerModelClassNumOutputNodes > 0) {
-//       sizes[3] = mNnClusterizerBatchedMode * mNnClusterizerModelClassNumOutputNodes; // modelProbabilities
-//     }
-//     if (!mNnClusterizerUseCfRegression) {
-//       if (mNnClusterizerModelReg1NumOutputNodes > 0) {
-//         sizes[4] = mNnClusterizerBatchedMode * mNnClusterizerModelReg1NumOutputNodes; // outputDataReg1
-//       }
-//       if (mNnClusterizerModelReg2NumOutputNodes > 0) {
-//         sizes[5] = mNnClusterizerBatchedMode * mNnClusterizerModelReg2NumOutputNodes; // outputDataReg2
-//       }
-//     }
-//   }
-//   if (mNnClusterizerTotalClusters > 0) {
-//     sizes[6] = mNnClusterizerTotalClusters; // mOutputDataClass
-//   }
-//   return sizes;
-// }
-
 void GPUTPCNNClusterizer::RegisterMemoryAllocation()
 {
   AllocateAndInitializeLate();

GPU/GPUTracking/TPCClusterFinder/GPUTPCNNClusterizer.h

@@ -37,31 +37,51 @@ class GPUTPCNNClusterizer : public GPUProcessor
 
   // Neural network clusterization
 
-  int mNnClusterizerSizeInputRow = 3;
-  int mNnClusterizerSizeInputPad = 3;
-  int mNnClusterizerSizeInputTime = 3;
-  int mNnClusterizerElementSize = -1;
-  bool mNnClusterizerAddIndexData = true;
+  int32_t mNnClusterizerSizeInputRow = 3;
+  int32_t mNnClusterizerSizeInputPad = 3;
+  int32_t mNnClusterizerSizeInputTime = 3;
+  int32_t mNnClusterizerChargeArraySize = -1;
+  int32_t mNnClusterizerElementSize = -1;
+  int8_t mNnClusterizerAddIndexData = 1;
   float mNnClassThreshold = 0.01;
-  bool mNnSigmoidTrafoClassThreshold = 1;
-  bool mNnClusterizerSetDeconvolutionFlags = true;
-  int mNnClusterizerUseCfRegression = 0;
-  int mNnClusterizerBatchedMode = 1;
-  int mNnClusterizerTotalClusters = 1;
-  int mNnClusterizerVerbosity = 0;
-  int mNnClusterizerBoundaryFillValue = -1;
-  int mNnClusterizerModelClassNumOutputNodes = -1;
-  int mNnClusterizerModelReg1NumOutputNodes = -1;
-  int mNnClusterizerModelReg2NumOutputNodes = -1;
-  int mNnInferenceInputDType = 0;  // 0: float16, 1: float32
-  int mNnInferenceOutputDType = 0; // 0: float16, 1: float32
-  int mISector = -1;
-  int mDeviceId = -1;
+  int8_t mNnSigmoidTrafoClassThreshold = 1;
+  int8_t mNnClusterizerSetDeconvolutionFlags = 1;
+  int32_t mNnClusterizerUseCfRegression = 0;
+  int32_t mNnClusterizerBatchedMode = 1;
+  int32_t mNnClusterizerTotalClusters = 1;
+  int32_t mNnClusterizerVerbosity = 0;
+  int32_t mNnClusterizerBoundaryFillValue = -1;
+  int32_t mNnClusterizerModelClassNumOutputNodes = -1;
+  int32_t mNnClusterizerModelReg1NumOutputNodes = -1;
+  int32_t mNnClusterizerModelReg2NumOutputNodes = -1;
+  int32_t mNnInferenceInputDType = 0;  // 0: float16, 1: float32
+  int32_t mNnInferenceOutputDType = 0; // 0: float16, 1: float32
+  int32_t mISector = -1;
+  int32_t mDeviceId = -1;
+
+  // GPU optimizations
+  uint32_t mNnClusterizerFullRowSize = 0;
+  uint32_t mNnClusterizerFullPadSize = 0;
+  uint32_t mNnClusterizerFullTimeSize = 0;
+  uint32_t mNnClusterizerPadTimeSize = 0;
+  uint32_t mNnClusterizerRowTimeSize = 0;
+  uint32_t mNnClusterizerRowTimeSizeFull = 0;
+
+  // Boundary lookup table
+  // int32_t mBoundaryMapSizeRow = 0;
+  // int32_t mBoundaryMapSizePadsPerRow = 0;
+  // int32_t mBoundaryMapSize = 0;
+  // int32_t mBoundaryPadding = 11; // Padding on each side of the boundary map to account for pad_offset
+  // int8_t* mIsBoundary = nullptr;
+
+  // Index lookup table
+  // int32_t mIndexLookupSize = 0;
+  // int32_t* mIndexLookup = nullptr;
 
   // Memory allocation for neural network
 
-  bool* mClusterFlags = nullptr; // mSplitInTime, mSplitInPad. Techincally both flags are set in the same way -> ClusterAccumulator.cx=nullptr
-  int* mOutputDataClass = nullptr;
+  int8_t* mClusterFlags = nullptr; // mSplitInTime, mSplitInPad. Techincally both flags are set in the same way -> ClusterAccumulator.cx=nullptr
+  int32_t* mOutputDataClass = nullptr;
 
   // FP32
   float* mInputData_32 = nullptr;

GPU/GPUTracking/TPCClusterFinder/GPUTPCNNClusterizerHost.cxx

@@ -19,6 +19,8 @@
 #include "GPUSettings.h"
 #include "ML/3rdparty/GPUORTFloat16.h"
 #include "GPUReconstruction.h"
+#include "GPUTPCGeometry.h"
+#include "DataFormatsTPC/Constants.h"
 
 #ifdef GPUCA_HAS_ONNX
 #include <onnxruntime_cxx_api.h>
@@ -87,8 +89,20 @@ void GPUTPCNNClusterizerHost::initClusterizer(const GPUSettingsProcessingNNclust
   clustererNN.mNnClusterizerSizeInputRow = settings.nnClusterizerSizeInputRow;
   clustererNN.mNnClusterizerSizeInputPad = settings.nnClusterizerSizeInputPad;
   clustererNN.mNnClusterizerSizeInputTime = settings.nnClusterizerSizeInputTime;
+  clustererNN.mNnClusterizerFullRowSize = 2 * settings.nnClusterizerSizeInputRow + 1;
+  clustererNN.mNnClusterizerFullPadSize = 2 * settings.nnClusterizerSizeInputPad + 1;
+  clustererNN.mNnClusterizerFullTimeSize = 2 * settings.nnClusterizerSizeInputTime + 1;
+  clustererNN.mNnClusterizerChargeArraySize = clustererNN.mNnClusterizerFullRowSize * clustererNN.mNnClusterizerFullPadSize * clustererNN.mNnClusterizerFullTimeSize;
+  clustererNN.mNnClusterizerPadTimeSize = clustererNN.mNnClusterizerFullPadSize * clustererNN.mNnClusterizerFullTimeSize;
+  clustererNN.mNnClusterizerRowTimeSize = clustererNN.mNnClusterizerFullRowSize * clustererNN.mNnClusterizerFullTimeSize;
+  clustererNN.mNnClusterizerRowTimeSizeFull = clustererNN.mNnClusterizerRowTimeSize + (settings.nnClusterizerAddIndexData ? 3 : 0);
+  clustererNN.mNnClusterizerElementSize = clustererNN.mNnClusterizerChargeArraySize + (settings.nnClusterizerAddIndexData ? 3 : 0);
+  // clustererNN.mBoundaryMapSizeRow = 3 * clustererNN.mNnClusterizerSizeInputRow + o2::tpc::constants::MAXGLOBALPADROW;
+  // clustererNN.mBoundaryPadding = 11; // padding on each side to account for pad_offset. N=11 since then mIsBoundary = 24320 ~< (1.5 x 2^14 = 24576) && N must be bigger than (NPads[row(end_iroc + 1)] - NPads[row(end_iroc)])/2 (=6) for pad_offset to work
+  // clustererNN.mBoundaryMapSizePadsPerRow = GPUTPCGeometry::NPads(o2::tpc::constants::MAXGLOBALPADROW - 1) + 2 * clustererNN.mBoundaryPadding;
+  // clustererNN.mBoundaryMapSize = clustererNN.mBoundaryMapSizeRow * clustererNN.mBoundaryMapSizePadsPerRow;
+  // clustererNN.mIndexLookupSize = 3 * clustererNN.mNnClusterizerChargeArraySize; // local row, pad, time shift from flat index
   clustererNN.mNnClusterizerAddIndexData = settings.nnClusterizerAddIndexData;
-  clustererNN.mNnClusterizerElementSize = ((2 * settings.nnClusterizerSizeInputRow + 1) * (2 * settings.nnClusterizerSizeInputPad + 1) * (2 * settings.nnClusterizerSizeInputTime + 1)) + (settings.nnClusterizerAddIndexData ? 3 : 0);
   clustererNN.mNnClusterizerBatchedMode = settings.nnClusterizerBatchedMode;
   clustererNN.mNnClusterizerBoundaryFillValue = settings.nnClusterizerBoundaryFillValue;
   clustererNN.mNnSigmoidTrafoClassThreshold = settings.nnSigmoidTrafoClassThreshold;
@@ -116,6 +130,39 @@ void GPUTPCNNClusterizerHost::initClusterizer(const GPUSettingsProcessingNNclust
   }
 }
 
+// void GPUTPCNNClusterizerHost::createBoundary(GPUTPCNNClusterizer& clustererNN)
+// {
+//   // Call after init of the clustererNN elements
+//   for (int r = 0; r < clustererNN.mBoundaryMapSizeRow; r++) {
+//     int8_t skipCheckInRow = 0;
+//     for (int p = 0; p < clustererNN.mBoundaryMapSizePadsPerRow; p++) {
+//       int32_t i = r * clustererNN.mBoundaryMapSizePadsPerRow + p;
+//       clustererNN.mIsBoundary[i] = 1;
+//       if (!skipCheckInRow && (p >= clustererNN.mBoundaryPadding || r >= clustererNN.mNnClusterizerSizeInputRow)) {
+//         if (r < (GPUTPCGeometry::EndIROC() + clustererNN.mNnClusterizerSizeInputRow)) {
+//           clustererNN.mIsBoundary[i] = (int32_t)((p - clustererNN.mBoundaryPadding) >= static_cast<int>(GPUTPCGeometry::NPads(r - clustererNN.mNnClusterizerSizeInputRow)));
+//         } else if (r >= (GPUTPCGeometry::EndIROC() + 2 * clustererNN.mNnClusterizerSizeInputRow) && r < (o2::tpc::constants::MAXGLOBALPADROW + 2 * clustererNN.mNnClusterizerSizeInputRow)) {
+//           clustererNN.mIsBoundary[i] = (int32_t)((p - clustererNN.mBoundaryPadding) >= static_cast<int>(GPUTPCGeometry::NPads(r - 2 * clustererNN.mNnClusterizerSizeInputRow)));
+//         }
+//         skipCheckInRow = (clustererNN.mIsBoundary[i] == 1); // No need to check further pads in this row
+//       }
+//     }
+//   }
+// }
+
+// void GPUTPCNNClusterizerHost::createIndexLookup(GPUTPCNNClusterizer& clustererNN)
+// {
+//   for (int32_t i = 0; i < clustererNN.mNnClusterizerChargeArraySize; i++) {
+//     int32_t r = CAMath::Floor(i / ((2 * clustererNN.mNnClusterizerSizeInputPad + 1) * (2 * clustererNN.mNnClusterizerSizeInputTime + 1))) - clustererNN.mNnClusterizerSizeInputRow;
+//     int32_t rest_1 = i % ((2 * clustererNN.mNnClusterizerSizeInputPad + 1) * (2 * clustererNN.mNnClusterizerSizeInputTime + 1));
+//     int32_t p = CAMath::Floor(rest_1 / (2 * clustererNN.mNnClusterizerSizeInputTime + 1)) - clustererNN.mNnClusterizerSizeInputPad;
+//     int32_t t = (rest_1 % (2 * clustererNN.mNnClusterizerSizeInputTime + 1)) - clustererNN.mNnClusterizerSizeInputTime;
+//     clustererNN.mIndexLookup[3 * i] = r;
+//     clustererNN.mIndexLookup[3 * i + 1] = p;
+//     clustererNN.mIndexLookup[3 * i + 2] = t;
+//   }
+// }
+
 // MockedOrtAllocator implementation to be able to use volatile assignment
 struct MockedOrtAllocator : OrtAllocator {
   MockedOrtAllocator(GPUReconstruction* = nullptr, OrtMemoryInfo* = nullptr);

GPU/GPUTracking/TPCClusterFinder/GPUTPCNNClusterizerHost.h

@@ -49,6 +49,8 @@ class GPUTPCNNClusterizerHost
 
   void init(const GPUSettingsProcessingNNclusterizer&);
   void initClusterizer(const GPUSettingsProcessingNNclusterizer&, GPUTPCNNClusterizer&);
+  void createBoundary(GPUTPCNNClusterizer&);
+  void createIndexLookup(GPUTPCNNClusterizer&);
 
   // ONNX
   void directOrtAllocator(Ort::Env*, Ort::MemoryInfo*, GPUReconstruction*, bool = false);