mirror of
https://github.com/claunia/cuetools.net.git
synced 2025-12-16 18:14:25 +00:00
optimizations
This commit is contained in:
chudov
@@ -95,20 +95,23 @@ namespace CUETools.Codecs.FlaCuda
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CUDA cuda;
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CUfunction cudaComputeAutocor;
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CUfunction cudaComputeLPC;
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CUfunction cudaEstimateResidual;
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CUfunction cudaSumResidual;
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CUfunction cudaEncodeResidual;
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CUdeviceptr cudaSamples;
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CUdeviceptr cudaWindow;
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CUdeviceptr cudaAutocorTasks;
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CUdeviceptr cudaAutocorOutput;
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CUdeviceptr cudaCompLPCOutput;
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CUdeviceptr cudaResidualTasks;
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CUdeviceptr cudaResidualOutput;
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IntPtr samplesBufferPtr = IntPtr.Zero;
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IntPtr autocorTasksPtr = IntPtr.Zero;
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IntPtr compLPCOutputPtr = IntPtr.Zero;
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IntPtr residualTasksPtr = IntPtr.Zero;
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IntPtr residualOutputPtr = IntPtr.Zero;
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CUstream cudaStream;
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CUstream cudaStream1;
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int nResidualTasks = 0;
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int nAutocorTasks = 0;
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const int MAX_BLOCKSIZE = 8192;
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const int maxResidualParts = MAX_BLOCKSIZE / (256 - 32);
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@@ -213,15 +216,13 @@ namespace CUETools.Codecs.FlaCuda
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cuda.Free(cudaSamples);
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cuda.Free(cudaAutocorTasks);
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cuda.Free(cudaAutocorOutput);
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cuda.Free(cudaCompLPCOutput);
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cuda.Free(cudaResidualTasks);
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cuda.Free(cudaResidualOutput);
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CUDADriver.cuMemFreeHost(compLPCOutputPtr);
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CUDADriver.cuMemFreeHost(residualOutputPtr);
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CUDADriver.cuMemFreeHost(samplesBufferPtr);
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CUDADriver.cuMemFreeHost(residualTasksPtr);
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CUDADriver.cuMemFreeHost(autocorTasksPtr);
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cuda.DestroyStream(cudaStream);
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cuda.DestroyStream(cudaStream1);
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cuda.Dispose();
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inited = false;
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}
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@@ -247,15 +248,13 @@ namespace CUETools.Codecs.FlaCuda
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cuda.Free(cudaSamples);
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cuda.Free(cudaAutocorTasks);
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cuda.Free(cudaAutocorOutput);
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cuda.Free(cudaCompLPCOutput);
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cuda.Free(cudaResidualTasks);
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cuda.Free(cudaResidualOutput);
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CUDADriver.cuMemFreeHost(compLPCOutputPtr);
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CUDADriver.cuMemFreeHost(residualOutputPtr);
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CUDADriver.cuMemFreeHost(samplesBufferPtr);
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CUDADriver.cuMemFreeHost(residualTasksPtr);
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CUDADriver.cuMemFreeHost(autocorTasksPtr);
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cuda.DestroyStream(cudaStream);
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cuda.DestroyStream(cudaStream1);
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cuda.Dispose();
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inited = false;
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}
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@@ -898,6 +897,65 @@ namespace CUETools.Codecs.FlaCuda
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_windowcount++;
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}
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unsafe void initialize_autocorTasks(int channelsCount, int max_order)
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{
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computeAutocorTaskStruct* autocorTasks = (computeAutocorTaskStruct*)autocorTasksPtr;
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encodeResidualTaskStruct* residualTasks = (encodeResidualTaskStruct*)residualTasksPtr;
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nAutocorTasks = 0;
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nResidualTasks = 0;
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for (int ch = 0; ch < channelsCount; ch++)
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for (int iWindow = 0; iWindow < _windowcount; iWindow++)
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{
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// Autocorelation task
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autocorTasks[nAutocorTasks].samplesOffs = ch * FlaCudaWriter.MAX_BLOCKSIZE;
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autocorTasks[nAutocorTasks].windowOffs = iWindow * 2 * FlaCudaWriter.MAX_BLOCKSIZE;
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nAutocorTasks++;
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// LPC tasks
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for (int order = 1; order <= max_order; order++)
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{
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residualTasks[nResidualTasks].residualOrder = order - 1;
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residualTasks[nResidualTasks].samplesOffs = ch * FlaCudaWriter.MAX_BLOCKSIZE;
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nResidualTasks++;
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}
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}
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// Fixed prediction
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for (int ch = 0; ch < channelsCount; ch++)
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{
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for (int order = 1; order <= 4; order++)
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{
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residualTasks[nResidualTasks].residualOrder = order - 1;
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residualTasks[nResidualTasks].samplesOffs = ch * FlaCudaWriter.MAX_BLOCKSIZE;
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residualTasks[nResidualTasks].shift = 0;
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switch (order)
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{
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case 1:
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residualTasks[nResidualTasks].coefs[0] = 1;
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break;
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case 2:
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residualTasks[nResidualTasks].coefs[0] = 2;
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residualTasks[nResidualTasks].coefs[1] = -1;
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break;
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case 3:
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residualTasks[nResidualTasks].coefs[0] = 3;
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residualTasks[nResidualTasks].coefs[1] = -3;
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residualTasks[nResidualTasks].coefs[2] = 1;
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break;
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case 4:
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residualTasks[nResidualTasks].coefs[0] = 4;
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residualTasks[nResidualTasks].coefs[1] = -6;
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residualTasks[nResidualTasks].coefs[2] = 4;
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residualTasks[nResidualTasks].coefs[3] = -1;
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break;
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}
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nResidualTasks++;
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}
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}
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cuda.CopyHostToDeviceAsync(cudaAutocorTasks, autocorTasksPtr, (uint)(sizeof(computeAutocorTaskStruct) * nAutocorTasks), cudaStream);
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cuda.CopyHostToDeviceAsync(cudaResidualTasks, residualTasksPtr, (uint)(sizeof(encodeResidualTaskStruct) * nResidualTasks), cudaStream);
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cuda.SynchronizeStream(cudaStream);
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}
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unsafe void encode_residual(FlacFrame frame)
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{
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for (int ch = 0; ch < channels; ch++)
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@@ -968,19 +1026,8 @@ namespace CUETools.Codecs.FlaCuda
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for (int order = 1; order <= max_order && order < frame.blocksize; order++)
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{
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int index = (order - 1) + max_order * (iWindow + _windowcount * ch);
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int nbits = 0;
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for (int p = 0; p < partCount; p++)
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nbits += ((int*)residualOutputPtr)[p + partCount * index];
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int cbits = 1;
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for (int i = order; i > 0; i--)
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{
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int c = residualTasks[index].coefs[i - 1];
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while (cbits < 16 && c != (c << (32 - cbits)) >> (32 - cbits))
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cbits++;
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}
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nbits += order * (int)frame.subframes[ch].obits + 4 + 5 + order * cbits + 6;
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int cbits = residualTasks[index].cbits;
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int nbits = order * (int)frame.subframes[ch].obits + 4 + 5 + order * cbits + 6 + residualTasks[index].size;
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if (frame.subframes[ch].best.size > nbits)
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{
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frame.subframes[ch].best.type = SubframeType.LPC;
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@@ -989,8 +1036,8 @@ namespace CUETools.Codecs.FlaCuda
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frame.subframes[ch].best.window = iWindow;
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frame.subframes[ch].best.cbits = cbits;
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frame.subframes[ch].best.shift = residualTasks[index].shift;
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fixed (int* fcoefs = frame.subframes[ch].best.coefs)
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AudioSamples.MemCpy(fcoefs, residualTasks[index].coefs, order);
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for (int i = 0; i < order; i++)
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frame.subframes[ch].best.coefs[i] = residualTasks[index].coefs[i];//order - 1 - i];
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}
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}
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}
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@@ -1002,10 +1049,7 @@ namespace CUETools.Codecs.FlaCuda
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for (int order = 1; order <= 4 && order < frame.blocksize; order++)
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{
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int index = (order - 1) + 4 * ch;
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int nbits = 0;
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for (int p = 0; p < partCount; p++)
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nbits += ((int*)residualOutputPtr)[p + partCount * (index + max_order * _windowcount * channelsCount)];
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nbits += order * (int)frame.subframes[ch].obits + 6;
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int nbits = order * (int)frame.subframes[ch].obits + 6 + residualTasks[index + max_order * _windowcount * channelsCount].size;
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if (frame.subframes[ch].best.size > nbits)
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{
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frame.subframes[ch].best.type = SubframeType.Fixed;
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@@ -1018,11 +1062,10 @@ namespace CUETools.Codecs.FlaCuda
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unsafe void estimate_residual(FlacFrame frame, int channelsCount, int max_order, int autocorPartCount, out int partCount)
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{
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encodeResidualTaskStruct* residualTasks = (encodeResidualTaskStruct*)residualTasksPtr;
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uint cbits = get_precision(frame.blocksize) + 1;
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int nResidualTasks = 0;
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int residualThreads = 256;
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int partSize = residualThreads - max_order;
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partSize &= 0xffffff0;
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partCount = (frame.blocksize + partSize - 1) / partSize;
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if (partCount > maxResidualParts)
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@@ -1031,92 +1074,24 @@ namespace CUETools.Codecs.FlaCuda
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if (frame.blocksize <= 4)
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return;
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// LPC
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for (int ch = 0; ch < channelsCount; ch++)
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for (int iWindow = 0; iWindow < _windowcount; iWindow++)
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{
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//int* lpcs = ((int*)compLPCOutputPtr) + (max_order + 1) * max_order * (iWindow + _windowcount * ch);
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//for (int order = 1; order <= max_order; order++)
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//{
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// residualTasks[nResidualTasks].residualOrder = order - 1;
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// residualTasks[nResidualTasks].samplesOffs = ch * FlaCudaWriter.MAX_BLOCKSIZE;
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// residualTasks[nResidualTasks].shift = lpcs[order + (order - 1) * (max_order + 1)];
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// AudioSamples.MemCpy(residualTasks[nResidualTasks].coefs, lpcs + (order - 1) * (max_order + 1), order);
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// nResidualTasks++;
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//}
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float* lpcs = ((float*)compLPCOutputPtr) + max_order * max_order * (iWindow + _windowcount * ch);
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for (int order = 1; order <= max_order; order++)
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{
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residualTasks[nResidualTasks].residualOrder = order - 1;
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residualTasks[nResidualTasks].samplesOffs = ch * FlaCudaWriter.MAX_BLOCKSIZE;
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cuda.SetParameter(cudaEstimateResidual, 0, (uint)cudaResidualOutput.Pointer);
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cuda.SetParameter(cudaEstimateResidual, IntPtr.Size, (uint)cudaSamples.Pointer);
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cuda.SetParameter(cudaEstimateResidual, IntPtr.Size * 2, (uint)cudaResidualTasks.Pointer);
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cuda.SetParameter(cudaEstimateResidual, IntPtr.Size * 3, (uint)frame.blocksize);
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cuda.SetParameter(cudaEstimateResidual, IntPtr.Size * 3 + sizeof(uint), (uint)partSize);
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cuda.SetParameterSize(cudaEstimateResidual, (uint)(IntPtr.Size * 3) + sizeof(uint) * 2U);
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cuda.SetFunctionBlockShape(cudaEstimateResidual, residualThreads, 1, 1);
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lpc.quantize_lpc_coefs(lpcs + (order - 1) * max_order,
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order, cbits, residualTasks[nResidualTasks].coefs,
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out residualTasks[nResidualTasks].shift, 15, 0);
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nResidualTasks++;
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}
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}
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// FIXED
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for (int ch = 0; ch < channelsCount; ch++)
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{
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for (int order = 1; order <= 4; order++)
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{
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residualTasks[nResidualTasks].residualOrder = order - 1;
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residualTasks[nResidualTasks].samplesOffs = ch * FlaCudaWriter.MAX_BLOCKSIZE;
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residualTasks[nResidualTasks].shift = 0;
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switch (order)
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{
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case 1:
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residualTasks[nResidualTasks].coefs[0] = 1;
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break;
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case 2:
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residualTasks[nResidualTasks].coefs[0] = 2;
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residualTasks[nResidualTasks].coefs[1] = -1;
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break;
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case 3:
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residualTasks[nResidualTasks].coefs[0] = 3;
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residualTasks[nResidualTasks].coefs[1] = -3;
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residualTasks[nResidualTasks].coefs[2] = 1;
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break;
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case 4:
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residualTasks[nResidualTasks].coefs[0] = 4;
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residualTasks[nResidualTasks].coefs[1] = -6;
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residualTasks[nResidualTasks].coefs[2] = 4;
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residualTasks[nResidualTasks].coefs[3] = -1;
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break;
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}
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nResidualTasks++;
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}
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}
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cuda.SetParameter(cudaEncodeResidual, 0, (uint)cudaResidualOutput.Pointer);
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cuda.SetParameter(cudaEncodeResidual, IntPtr.Size, (uint)cudaSamples.Pointer);
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cuda.SetParameter(cudaEncodeResidual, IntPtr.Size * 2, (uint)cudaResidualTasks.Pointer);
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cuda.SetParameter(cudaEncodeResidual, IntPtr.Size * 3, (uint)frame.blocksize);
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cuda.SetParameter(cudaEncodeResidual, IntPtr.Size * 3 + sizeof(uint), (uint)partSize);
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cuda.SetParameterSize(cudaEncodeResidual, (uint)(IntPtr.Size * 3) + sizeof(uint) * 2U);
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cuda.SetFunctionBlockShape(cudaEncodeResidual, residualThreads, 1, 1);
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cuda.SetParameter(cudaSumResidual, 0, (uint)cudaResidualTasks.Pointer);
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cuda.SetParameter(cudaSumResidual, IntPtr.Size, (uint)cudaResidualOutput.Pointer);
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cuda.SetParameter(cudaSumResidual, IntPtr.Size * 2, (uint)partCount);
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cuda.SetParameterSize(cudaSumResidual, (uint)(IntPtr.Size * 2) + sizeof(uint) * 1U);
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cuda.SetFunctionBlockShape(cudaSumResidual, 64, 1, 1);
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// issue work to the GPU
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cuda.CopyHostToDeviceAsync(cudaResidualTasks, residualTasksPtr, (uint)(sizeof(encodeResidualTaskStruct) * nResidualTasks), cudaStream);
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cuda.LaunchAsync(cudaEncodeResidual, partCount, nResidualTasks, cudaStream);
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cuda.CopyDeviceToHostAsync(cudaResidualOutput, residualOutputPtr, (uint)(sizeof(int) * partCount * nResidualTasks), cudaStream);
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cuda.SynchronizeStream(cudaStream);
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}
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unsafe void initialize_autocorTasks()
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{
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computeAutocorTaskStruct* autocorTasks = (computeAutocorTaskStruct*)autocorTasksPtr;
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int nAutocorTasks = 0;
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for (int ch = 0; ch < (channels == 2 ? 4 : channels); ch++)
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for (int iWindow = 0; iWindow < _windowcount; iWindow++)
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{
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autocorTasks[nAutocorTasks].samplesOffs = ch * FlaCudaWriter.MAX_BLOCKSIZE;
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autocorTasks[nAutocorTasks].windowOffs = iWindow * 2 * FlaCudaWriter.MAX_BLOCKSIZE;
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nAutocorTasks++;
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}
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cuda.CopyHostToDeviceAsync(cudaAutocorTasks, autocorTasksPtr, (uint)(sizeof(computeAutocorTaskStruct) * nAutocorTasks), cudaStream);
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cuda.LaunchAsync(cudaEstimateResidual, partCount, nResidualTasks, cudaStream);
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cuda.LaunchAsync(cudaSumResidual, 1, nResidualTasks, cudaStream);
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cuda.CopyDeviceToHostAsync(cudaResidualTasks, residualTasksPtr, (uint)(sizeof(encodeResidualTaskStruct) * nResidualTasks), cudaStream);
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cuda.SynchronizeStream(cudaStream);
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}
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@@ -1124,7 +1099,7 @@ namespace CUETools.Codecs.FlaCuda
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{
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int autocorThreads = 256;
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int partSize = 2 * autocorThreads - max_order;
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int nAutocorTasks = _windowcount * channelsCount;
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partSize &= 0xffffff0;
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partCount = (frame.blocksize + partSize - 1) / partSize;
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if (partCount > maxAutocorParts)
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@@ -1143,20 +1118,20 @@ namespace CUETools.Codecs.FlaCuda
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cuda.SetParameterSize(cudaComputeAutocor, (uint)(IntPtr.Size * 4) + sizeof(uint) * 3);
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cuda.SetFunctionBlockShape(cudaComputeAutocor, autocorThreads, 1, 1);
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cuda.SetParameter(cudaComputeLPC, 0, (uint)cudaCompLPCOutput.Pointer);
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cuda.SetParameter(cudaComputeLPC, 0, (uint)cudaResidualTasks.Pointer);
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cuda.SetParameter(cudaComputeLPC, IntPtr.Size, (uint)cudaAutocorOutput.Pointer);
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cuda.SetParameter(cudaComputeLPC, IntPtr.Size * 2, (uint)cudaAutocorTasks.Pointer);
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cuda.SetParameter(cudaComputeLPC, IntPtr.Size * 3, (uint)max_order);
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cuda.SetParameter(cudaComputeLPC, IntPtr.Size * 3 + sizeof(uint), (uint)partCount);
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cuda.SetParameterSize(cudaComputeLPC, (uint)(IntPtr.Size * 3) + sizeof(uint) * 2);
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cuda.SetFunctionBlockShape(cudaComputeLPC, 32, 1, 1);
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cuda.SetFunctionBlockShape(cudaComputeLPC, 64, 1, 1);
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// issue work to the GPU
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cuda.CopyHostToDeviceAsync(cudaSamples, samplesBufferPtr, (uint)(sizeof(int) * FlaCudaWriter.MAX_BLOCKSIZE * channelsCount), cudaStream);
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cuda.LaunchAsync(cudaComputeAutocor, partCount, nAutocorTasks, cudaStream);
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cuda.LaunchAsync(cudaComputeLPC, 1, nAutocorTasks, cudaStream);
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cuda.CopyDeviceToHostAsync(cudaCompLPCOutput, compLPCOutputPtr, (uint)(sizeof(float) * (max_order + 1) * max_order * nAutocorTasks), cudaStream);
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cuda.SynchronizeStream(cudaStream);
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//cuda.CopyDeviceToHostAsync(cudaResidualTasks, residualTasksPtr, (uint)(sizeof(encodeResidualTaskStruct) * nResidualTasks), cudaStream1);
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}
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unsafe int encode_frame(out int size)
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@@ -1167,6 +1142,9 @@ namespace CUETools.Codecs.FlaCuda
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{
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frame.InitSize(eparams.block_size, eparams.variable_block_size != 0);
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bool doMidside = channels == 2 && eparams.do_midside;
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int channelCount = doMidside ? 2 * channels : channels;
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if (frame.blocksize != _windowsize && frame.blocksize > 4)
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{
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_windowsize = frame.blocksize;
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@@ -1179,11 +1157,9 @@ namespace CUETools.Codecs.FlaCuda
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if (_windowcount == 0)
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throw new Exception("invalid windowfunction");
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cuda.CopyHostToDevice<float>(cudaWindow, windowBuffer);
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initialize_autocorTasks();
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initialize_autocorTasks(channelCount, eparams.max_prediction_order);
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}
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bool doMidside = channels == 2 && eparams.do_midside;
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int channelCount = doMidside ? 2 * channels : channels;
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if (doMidside)
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channel_decorrelation(s, s + FlaCudaWriter.MAX_BLOCKSIZE, s + 2 * FlaCudaWriter.MAX_BLOCKSIZE, s + 3 * FlaCudaWriter.MAX_BLOCKSIZE, frame.blocksize);
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@@ -1296,33 +1272,29 @@ namespace CUETools.Codecs.FlaCuda
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cuda.LoadModule(System.IO.Path.Combine(Environment.CurrentDirectory, "flacuda.cubin"));
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cudaComputeAutocor = cuda.GetModuleFunction("cudaComputeAutocor");
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cudaComputeLPC = cuda.GetModuleFunction("cudaComputeLPC");
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cudaEstimateResidual = cuda.GetModuleFunction("cudaEstimateResidual");
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cudaSumResidual = cuda.GetModuleFunction("cudaSumResidual");
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cudaEncodeResidual = cuda.GetModuleFunction("cudaEncodeResidual");
|
||||
cudaSamples = cuda.Allocate((uint)(sizeof(int) * FlaCudaWriter.MAX_BLOCKSIZE * (channels == 2 ? 4 : channels)));
|
||||
cudaWindow = cuda.Allocate((uint)sizeof(float) * FlaCudaWriter.MAX_BLOCKSIZE * 2 * lpc.MAX_LPC_WINDOWS);
|
||||
cudaAutocorTasks = cuda.Allocate((uint)(sizeof(computeAutocorTaskStruct) * (channels == 2 ? 4 : channels) * lpc.MAX_LPC_WINDOWS));
|
||||
cudaAutocorOutput = cuda.Allocate((uint)(sizeof(float) * (lpc.MAX_LPC_ORDER + 1) * (channels == 2 ? 4 : channels) * lpc.MAX_LPC_WINDOWS) * maxAutocorParts);
|
||||
cudaCompLPCOutput = cuda.Allocate((uint)(sizeof(float) * lpc.MAX_LPC_ORDER * lpc.MAX_LPC_ORDER * (channels == 2 ? 4 : channels) * lpc.MAX_LPC_WINDOWS) * maxAutocorParts);
|
||||
cudaResidualTasks = cuda.Allocate((uint)(sizeof(encodeResidualTaskStruct) * (channels == 2 ? 4 : channels) * lpc.MAX_LPC_ORDER * lpc.MAX_LPC_WINDOWS));
|
||||
cudaResidualTasks = cuda.Allocate((uint)(sizeof(encodeResidualTaskStruct) * (channels == 2 ? 4 : channels) * (lpc.MAX_LPC_ORDER * lpc.MAX_LPC_WINDOWS + 4)));
|
||||
cudaResidualOutput = cuda.Allocate((uint)(sizeof(int) * (channels == 2 ? 4 : channels) * (lpc.MAX_LPC_ORDER + 1) * lpc.MAX_LPC_WINDOWS * maxResidualParts));
|
||||
CUResult cuErr = CUDADriver.cuMemAllocHost(ref samplesBufferPtr, (uint)(sizeof(int) * (channels == 2 ? 4 : channels) * FlaCudaWriter.MAX_BLOCKSIZE));
|
||||
if (cuErr == CUResult.Success)
|
||||
cuErr = CUDADriver.cuMemAllocHost(ref autocorTasksPtr, (uint)(sizeof(computeAutocorTaskStruct) * (channels == 2 ? 4 : channels) * lpc.MAX_LPC_WINDOWS));
|
||||
if (cuErr == CUResult.Success)
|
||||
cuErr = CUDADriver.cuMemAllocHost(ref compLPCOutputPtr, (uint)(sizeof(float) * (lpc.MAX_LPC_ORDER + 1) * lpc.MAX_LPC_ORDER * (channels == 2 ? 4 : channels) * lpc.MAX_LPC_WINDOWS));
|
||||
if (cuErr == CUResult.Success)
|
||||
cuErr = CUDADriver.cuMemAllocHost(ref residualTasksPtr, (uint)(sizeof(encodeResidualTaskStruct) * (channels == 2 ? 4 : channels) * lpc.MAX_LPC_WINDOWS * lpc.MAX_LPC_ORDER));
|
||||
if (cuErr == CUResult.Success)
|
||||
cuErr = CUDADriver.cuMemAllocHost(ref residualOutputPtr, (uint)(sizeof(int) * (channels == 2 ? 4 : channels) * lpc.MAX_LPC_WINDOWS * lpc.MAX_LPC_ORDER * maxResidualParts));
|
||||
cuErr = CUDADriver.cuMemAllocHost(ref residualTasksPtr, (uint)(sizeof(encodeResidualTaskStruct) * (channels == 2 ? 4 : channels) * (lpc.MAX_LPC_WINDOWS * lpc.MAX_LPC_ORDER + 4)));
|
||||
if (cuErr != CUResult.Success)
|
||||
{
|
||||
if (samplesBufferPtr != IntPtr.Zero) CUDADriver.cuMemFreeHost(samplesBufferPtr); samplesBufferPtr = IntPtr.Zero;
|
||||
if (autocorTasksPtr != IntPtr.Zero) CUDADriver.cuMemFreeHost(autocorTasksPtr); autocorTasksPtr = IntPtr.Zero;
|
||||
if (compLPCOutputPtr != IntPtr.Zero) CUDADriver.cuMemFreeHost(compLPCOutputPtr); compLPCOutputPtr = IntPtr.Zero;
|
||||
if (residualTasksPtr != IntPtr.Zero) CUDADriver.cuMemFreeHost(residualTasksPtr); residualTasksPtr = IntPtr.Zero;
|
||||
if (residualOutputPtr != IntPtr.Zero) CUDADriver.cuMemFreeHost(residualOutputPtr); residualOutputPtr = IntPtr.Zero;
|
||||
throw new CUDAException(cuErr);
|
||||
}
|
||||
cudaStream = cuda.CreateStream();
|
||||
cudaStream1 = cuda.CreateStream();
|
||||
if (_IO == null)
|
||||
_IO = new FileStream(_path, FileMode.Create, FileAccess.Write, FileShare.Read);
|
||||
int header_size = flake_encode_init();
|
||||
@@ -1769,7 +1741,9 @@ namespace CUETools.Codecs.FlaCuda
|
||||
public int residualOrder;
|
||||
public int samplesOffs;
|
||||
public int shift;
|
||||
public int reserved;
|
||||
public int cbits;
|
||||
public int size;
|
||||
public fixed int reserved[11];
|
||||
public fixed int coefs[32];
|
||||
};
|
||||
}
|
||||
|
||||
@@ -26,6 +26,17 @@ typedef struct
|
||||
int windowOffs;
|
||||
} computeAutocorTaskStruct;
|
||||
|
||||
typedef struct
|
||||
{
|
||||
int residualOrder; // <= 32
|
||||
int samplesOffs;
|
||||
int shift;
|
||||
int cbits;
|
||||
int size;
|
||||
int reserved[11];
|
||||
int coefs[32];
|
||||
} encodeResidualTaskStruct;
|
||||
|
||||
extern "C" __global__ void cudaComputeAutocor(
|
||||
float *output,
|
||||
const int *samples,
|
||||
@@ -83,11 +94,11 @@ extern "C" __global__ void cudaComputeAutocor(
|
||||
}
|
||||
|
||||
extern "C" __global__ void cudaComputeLPC(
|
||||
float*output,
|
||||
encodeResidualTaskStruct *output,
|
||||
float*autoc,
|
||||
computeAutocorTaskStruct *tasks,
|
||||
int max_order, // should be <= 32
|
||||
int partCount // should be <= blockDim
|
||||
int partCount // should be <= blockDim?
|
||||
)
|
||||
{
|
||||
__shared__ struct {
|
||||
@@ -96,6 +107,7 @@ extern "C" __global__ void cudaComputeLPC(
|
||||
float buf[32];
|
||||
int bits[32];
|
||||
float autoc[33];
|
||||
int cbits;
|
||||
} shared;
|
||||
const int tid = threadIdx.x;
|
||||
|
||||
@@ -116,7 +128,7 @@ extern "C" __global__ void cudaComputeLPC(
|
||||
|
||||
__syncthreads();
|
||||
|
||||
if (tid <= 32)
|
||||
if (tid < 32)
|
||||
shared.tmp[tid] = 0.0f;
|
||||
|
||||
float err = shared.autoc[0];
|
||||
@@ -125,7 +137,7 @@ extern "C" __global__ void cudaComputeLPC(
|
||||
{
|
||||
if (tid < 32)
|
||||
{
|
||||
shared.buf[tid] = tid < order ? shared.tmp[tid] * shared.autoc[order - tid] : 0;
|
||||
shared.buf[tid] = (tid < order) * shared.tmp[tid] * shared.autoc[order - tid];
|
||||
shared.buf[tid] += shared.buf[tid + 16];
|
||||
shared.buf[tid] += shared.buf[tid + 8];
|
||||
shared.buf[tid] += shared.buf[tid + 4];
|
||||
@@ -138,38 +150,135 @@ extern "C" __global__ void cudaComputeLPC(
|
||||
|
||||
err *= 1.0f - (r * r);
|
||||
|
||||
if (tid == 0)
|
||||
shared.tmp[order] = r; // we could also set shared.tmp[-1] to 1.0f
|
||||
if (tid < order)
|
||||
shared.tmp[tid] += r * shared.tmp[order - 1 - tid];
|
||||
if (tid <= order)
|
||||
output[((blockIdx.x + blockIdx.y * gridDim.x) * max_order + order) * max_order + tid] = -shared.tmp[tid];
|
||||
//{
|
||||
// int precision = 13;
|
||||
// shared.bits[tid] = 32 - __clz(__float2int_rn(fabs(shared.tmp[tid]) * (1 << 15))) - precision;
|
||||
// shared.bits[tid] = max(shared.bits[tid], shared.bits[tid + 16]);
|
||||
// shared.bits[tid] = max(shared.bits[tid], shared.bits[tid + 8]);
|
||||
// shared.bits[tid] = max(shared.bits[tid], shared.bits[tid + 4]);
|
||||
// shared.bits[tid] = max(shared.bits[tid], shared.bits[tid + 2]);
|
||||
// shared.bits[tid] = max(shared.bits[tid], shared.bits[tid + 1]);
|
||||
// int sh = max(0,min(15, 15 - shared.bits[0]));
|
||||
// shared.bits[tid] = max(-(1 << precision),min((1 << precision)-1,__float2int_rn(-shared.tmp[tid] * (1 << sh))));
|
||||
// if (tid == 0)
|
||||
// output[((blockIdx.x + blockIdx.y * gridDim.x) * max_order + order) * (1 + max_order) + order + 1] = sh;
|
||||
// output[((blockIdx.x + blockIdx.y * gridDim.x) * max_order + order) * (1 + max_order) + tid] = shared.bits[tid];
|
||||
//}
|
||||
shared.tmp[tid] += (tid < order) * r * shared.tmp[order - 1 - tid] + (tid == order) * r;
|
||||
|
||||
if (tid < 32)
|
||||
{
|
||||
int precision = 13;
|
||||
shared.bits[tid] = __mul24((33 - __clz(__float2int_rn(fabs(shared.tmp[tid]) * (1 << 15))) - precision), tid <= order);
|
||||
shared.bits[tid] = max(shared.bits[tid], shared.bits[tid + 16]);
|
||||
shared.bits[tid] = max(shared.bits[tid], shared.bits[tid + 8]);
|
||||
shared.bits[tid] = max(shared.bits[tid], shared.bits[tid + 4]);
|
||||
shared.bits[tid] = max(shared.bits[tid], shared.bits[tid + 2]);
|
||||
shared.bits[tid] = max(shared.bits[tid], shared.bits[tid + 1]);
|
||||
int sh = max(0,min(15, 15 - shared.bits[0]));
|
||||
int coef = max(-(1 << precision),min((1 << precision)-1,__float2int_rn(-shared.tmp[tid] * (1 << sh))));
|
||||
if (tid <= order)
|
||||
output[(blockIdx.x + blockIdx.y * gridDim.x) * max_order + order].coefs[tid] = coef;
|
||||
if (tid == 0)
|
||||
output[(blockIdx.x + blockIdx.y * gridDim.x) * max_order + order].shift = sh;
|
||||
shared.bits[tid] = 33 - max(__clz(coef),__clz(-1 ^ coef));
|
||||
shared.bits[tid] = max(shared.bits[tid], shared.bits[tid + 16]);
|
||||
shared.bits[tid] = max(shared.bits[tid], shared.bits[tid + 8]);
|
||||
shared.bits[tid] = max(shared.bits[tid], shared.bits[tid + 4]);
|
||||
shared.bits[tid] = max(shared.bits[tid], shared.bits[tid + 2]);
|
||||
shared.bits[tid] = max(shared.bits[tid], shared.bits[tid + 1]);
|
||||
int cbits = shared.bits[0];
|
||||
if (tid == 0)
|
||||
output[(blockIdx.x + blockIdx.y * gridDim.x) * max_order + order].cbits = cbits;
|
||||
}
|
||||
__syncthreads();
|
||||
}
|
||||
}
|
||||
|
||||
typedef struct
|
||||
extern "C" __global__ void cudaEstimateResidual(
|
||||
int*output,
|
||||
int*samples,
|
||||
encodeResidualTaskStruct *tasks,
|
||||
int frameSize,
|
||||
int partSize // should be <= blockDim - max_order
|
||||
)
|
||||
{
|
||||
int residualOrder; // <= 32
|
||||
int samplesOffs;
|
||||
int shift;
|
||||
int reserved;
|
||||
int coefs[32];
|
||||
} encodeResidualTaskStruct;
|
||||
__shared__ struct {
|
||||
int data[256];
|
||||
int residual[256];
|
||||
int rice[32];
|
||||
encodeResidualTaskStruct task;
|
||||
} shared;
|
||||
const int tid = threadIdx.x;
|
||||
// fetch task data
|
||||
if (tid < sizeof(encodeResidualTaskStruct) / sizeof(int))
|
||||
((int*)&shared.task)[tid] = ((int*)(tasks + blockIdx.y))[tid];
|
||||
__syncthreads();
|
||||
const int pos = blockIdx.x * partSize;
|
||||
const int residualOrder = shared.task.residualOrder + 1;
|
||||
const int residualLen = min(frameSize - pos - residualOrder, partSize);
|
||||
const int dataLen = residualLen + residualOrder;
|
||||
|
||||
// fetch samples
|
||||
shared.data[tid] = (tid < dataLen ? samples[shared.task.samplesOffs + pos + tid] : 0);
|
||||
|
||||
// reverse coefs
|
||||
if (tid < residualOrder) shared.task.coefs[tid] = shared.task.coefs[residualOrder - 1 - tid];
|
||||
|
||||
// compute residual
|
||||
__syncthreads();
|
||||
long sum = 0;
|
||||
for (int c = 0; c < residualOrder; c++)
|
||||
sum += __mul24(shared.data[tid + c], shared.task.coefs[c]);
|
||||
int res = shared.data[tid + residualOrder] - (sum >> shared.task.shift);
|
||||
shared.residual[tid] = __mul24(tid < residualLen, (2 * res) ^ (res >> 31));
|
||||
|
||||
__syncthreads();
|
||||
// residual sum: reduction in shared mem
|
||||
if (tid < 128) shared.residual[tid] += shared.residual[tid + 128]; __syncthreads();
|
||||
if (tid < 64) shared.residual[tid] += shared.residual[tid + 64]; __syncthreads();
|
||||
if (tid < 32) shared.residual[tid] += shared.residual[tid + 32]; __syncthreads();
|
||||
shared.residual[tid] += shared.residual[tid + 16];
|
||||
shared.residual[tid] += shared.residual[tid + 8];
|
||||
shared.residual[tid] += shared.residual[tid + 4];
|
||||
shared.residual[tid] += shared.residual[tid + 2];
|
||||
shared.residual[tid] += shared.residual[tid + 1];
|
||||
__syncthreads();
|
||||
|
||||
if (tid < 32)
|
||||
{
|
||||
// rice parameter search
|
||||
shared.rice[tid] = __mul24(tid >= 15, 0x7fffff) + residualLen * (tid + 1) + ((shared.residual[0] - (residualLen >> 1)) >> tid);
|
||||
shared.rice[tid] = min(shared.rice[tid], shared.rice[tid + 8]);
|
||||
shared.rice[tid] = min(shared.rice[tid], shared.rice[tid + 4]);
|
||||
shared.rice[tid] = min(shared.rice[tid], shared.rice[tid + 2]);
|
||||
shared.rice[tid] = min(shared.rice[tid], shared.rice[tid + 1]);
|
||||
}
|
||||
if (tid == 0)
|
||||
output[blockIdx.x + blockIdx.y * gridDim.x] = shared.rice[0];
|
||||
}
|
||||
|
||||
extern "C" __global__ void cudaSumResidual(
|
||||
encodeResidualTaskStruct *tasks,
|
||||
int *residual,
|
||||
int partCount // <= blockDim.y (64)
|
||||
)
|
||||
{
|
||||
__shared__ struct {
|
||||
int partLen[64];
|
||||
//encodeResidualTaskStruct task;
|
||||
} shared;
|
||||
|
||||
const int tid = threadIdx.x;
|
||||
// fetch task data
|
||||
// if (tid < sizeof(encodeResidualTaskStruct) / sizeof(int))
|
||||
//((int*)&shared.task)[tid] = ((int*)(tasks + blockIdx.y))[tid];
|
||||
// __syncthreads();
|
||||
|
||||
shared.partLen[tid] = (tid < partCount) ? residual[tid + partCount * blockIdx.y] : 0;
|
||||
|
||||
__syncthreads();
|
||||
// length sum: reduction in shared mem
|
||||
if (tid < 32) shared.partLen[tid] += shared.partLen[tid + 32]; __syncthreads();
|
||||
shared.partLen[tid] += shared.partLen[tid + 16];
|
||||
shared.partLen[tid] += shared.partLen[tid + 8];
|
||||
shared.partLen[tid] += shared.partLen[tid + 4];
|
||||
shared.partLen[tid] += shared.partLen[tid + 2];
|
||||
shared.partLen[tid] += shared.partLen[tid + 1];
|
||||
__syncthreads();
|
||||
|
||||
// FIXME: should process partition order here!!!
|
||||
|
||||
// return sum
|
||||
if (tid == 0)
|
||||
tasks[blockIdx.y].size = shared.partLen[0];
|
||||
}
|
||||
|
||||
extern "C" __global__ void cudaEncodeResidual(
|
||||
int*output,
|
||||
@@ -233,5 +342,4 @@ extern "C" __global__ void cudaEncodeResidual(
|
||||
if (tid == 0)
|
||||
output[blockIdx.x + blockIdx.y * gridDim.x] = shared.rice[0];
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
Reference in New Issue
Block a user