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
@@ -54,9 +54,9 @@
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</ItemGroup>
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<ItemGroup>
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<None Include="flacuda.cu">
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<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
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<Generator>"%24%28CUDA_BIN_PATH%29\nvcc.exe" flacuda.cu --cubin -cbin "%24%28VCInstallDir%29bin"</Generator>
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</None>
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<EmbeddedResource Include="flacuda.cubin">
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</EmbeddedResource>
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</ItemGroup>
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<Import Project="$(MSBuildBinPath)\Microsoft.CSharp.targets" />
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<!-- To modify your build process, add your task inside one of the targets below and uncomment it.
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@@ -67,7 +67,8 @@
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</Target>
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-->
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<PropertyGroup>
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<PostBuildEvent>nvcc flacuda.cu --maxrregcount 10 --cubin --compiler-bindir "C:\Program Files (x86)\Microsoft Visual Studio 8\VC\bin" --system-include "C:\Program Files (x86)\Microsoft Visual Studio 8\VC\include"
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</PostBuildEvent>
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<PostBuildEvent>
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</PostBuildEvent>
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<PreBuildEvent>nvcc $(ProjectDir)flacuda.cu -o $(ProjectDir)\flacuda.cubin --machine 32 --maxrregcount 10 --cubin --compiler-bindir "C:\Program Files (x86)\Microsoft Visual Studio 8\VC\bin" --system-include "C:\Program Files (x86)\Microsoft Visual Studio 8\VC\include"</PreBuildEvent>
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</PropertyGroup>
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</Project>
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@@ -75,7 +75,7 @@ namespace CUETools.Codecs.FlaCuda
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float[] windowBuffer;
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int samplesInBuffer = 0;
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int _compressionLevel = 7;
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int _compressionLevel = 5;
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int _blocksize = 0;
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int _totalSize = 0;
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int _windowsize = 0, _windowcount = 0;
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@@ -96,6 +96,7 @@ namespace CUETools.Codecs.FlaCuda
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CUfunction cudaComputeAutocor;
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CUfunction cudaComputeLPC;
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CUfunction cudaEstimateResidual;
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CUfunction cudaSumResidualChunks;
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CUfunction cudaSumResidual;
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CUfunction cudaEncodeResidual;
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CUdeviceptr cudaSamples;
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@@ -104,6 +105,7 @@ namespace CUETools.Codecs.FlaCuda
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CUdeviceptr cudaAutocorOutput;
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CUdeviceptr cudaResidualTasks;
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CUdeviceptr cudaResidualOutput;
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CUdeviceptr cudaResidualSums;
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IntPtr samplesBufferPtr = IntPtr.Zero;
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IntPtr autocorTasksPtr = IntPtr.Zero;
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IntPtr residualTasksPtr = IntPtr.Zero;
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@@ -114,7 +116,7 @@ namespace CUETools.Codecs.FlaCuda
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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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const int maxResidualParts = 64;
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const int maxAutocorParts = MAX_BLOCKSIZE / (256 - 32);
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public FlaCudaWriter(string path, int bitsPerSample, int channelCount, int sampleRate, Stream IO)
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@@ -218,6 +220,7 @@ namespace CUETools.Codecs.FlaCuda
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cuda.Free(cudaAutocorOutput);
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cuda.Free(cudaResidualTasks);
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cuda.Free(cudaResidualOutput);
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cuda.Free(cudaResidualSums);
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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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@@ -250,6 +253,7 @@ namespace CUETools.Codecs.FlaCuda
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cuda.Free(cudaAutocorOutput);
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cuda.Free(cudaResidualTasks);
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cuda.Free(cudaResidualOutput);
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cuda.Free(cudaResidualSums);
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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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@@ -278,7 +282,11 @@ namespace CUETools.Codecs.FlaCuda
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public long BlockSize
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{
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set { _blocksize = (int)value; }
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set {
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if (value < 256 || value > MAX_BLOCKSIZE )
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throw new Exception("unsupported BlockSize value");
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_blocksize = (int)value;
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}
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get { return _blocksize == 0 ? eparams.block_size : _blocksize; }
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}
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@@ -911,9 +919,9 @@ namespace CUETools.Codecs.FlaCuda
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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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for (int order = 1; order <= ((max_order + 7) & ~7); order++)
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{
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residualTasks[nResidualTasks].residualOrder = order - 1;
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residualTasks[nResidualTasks].residualOrder = order <= max_order ? order : 0;
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residualTasks[nResidualTasks].samplesOffs = ch * FlaCudaWriter.MAX_BLOCKSIZE;
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nResidualTasks++;
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}
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@@ -921,9 +929,9 @@ namespace CUETools.Codecs.FlaCuda
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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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for (int order = 1; order <= 8; order++)
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{
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residualTasks[nResidualTasks].residualOrder = order - 1;
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residualTasks[nResidualTasks].residualOrder = order <= 4 ? order : 0;
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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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@@ -1025,9 +1033,11 @@ namespace CUETools.Codecs.FlaCuda
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{
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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 index = (order - 1) + ((max_order + 7) & ~7) * (iWindow + _windowcount * ch);
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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 (residualTasks[index].residualOrder != order)
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throw new Exception("oops");
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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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@@ -1048,8 +1058,10 @@ namespace CUETools.Codecs.FlaCuda
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{
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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 = order * (int)frame.subframes[ch].obits + 6 + residualTasks[index + max_order * _windowcount * channelsCount].size;
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int index = (order - 1) + 8 * ch + ((max_order + 7) & ~7) * _windowcount * channelsCount;
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int nbits = order * (int)frame.subframes[ch].obits + 6 + residualTasks[index].size;
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if (residualTasks[index].residualOrder != order)
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throw new Exception("oops");
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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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@@ -1062,34 +1074,47 @@ 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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if (frame.blocksize <= 4)
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{
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partCount = 0;
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return;
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}
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uint cbits = get_precision(frame.blocksize) + 1;
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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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int partSize = 256 - 32;
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partCount = (frame.blocksize + partSize - 1) / partSize;
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if (partCount > maxResidualParts)
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throw new Exception("internal error");
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throw new Exception("invalid combination of block size and LPC order");
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if (frame.blocksize <= 4)
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return;
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cuda.SetParameter(cudaEstimateResidual, sizeof(uint) * 0, (uint)cudaResidualOutput.Pointer);
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cuda.SetParameter(cudaEstimateResidual, sizeof(uint) * 1, (uint)cudaSamples.Pointer);
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cuda.SetParameter(cudaEstimateResidual, sizeof(uint) * 2, (uint)cudaResidualTasks.Pointer);
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cuda.SetParameter(cudaEstimateResidual, sizeof(uint) * 3, (uint)max_order);
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cuda.SetParameter(cudaEstimateResidual, sizeof(uint) * 4, (uint)frame.blocksize);
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cuda.SetParameter(cudaEstimateResidual, sizeof(uint) * 5, (uint)partSize);
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cuda.SetParameterSize(cudaEstimateResidual, sizeof(uint) * 6);
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cuda.SetFunctionBlockShape(cudaEstimateResidual, 64, 4, 1);
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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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//cuda.SetParameter(cudaSumResidualChunks, 0, (uint)cudaResidualSums.Pointer);
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//cuda.SetParameter(cudaSumResidualChunks, sizeof(uint), (uint)cudaResidualTasks.Pointer);
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//cuda.SetParameter(cudaSumResidualChunks, sizeof(uint) * 2, (uint)cudaResidualOutput.Pointer);
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//cuda.SetParameter(cudaSumResidualChunks, sizeof(uint) * 3, (uint)frame.blocksize);
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//cuda.SetParameter(cudaSumResidualChunks, sizeof(uint) * 4, (uint)partSize);
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//cuda.SetParameterSize(cudaSumResidualChunks, sizeof(uint) * 5U);
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//cuda.SetFunctionBlockShape(cudaSumResidualChunks, 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.SetParameter(cudaSumResidual, sizeof(uint), (uint)cudaResidualOutput.Pointer);
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cuda.SetParameter(cudaSumResidual, sizeof(uint) * 2, (uint)partSize);
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cuda.SetParameter(cudaSumResidual, sizeof(uint) * 3, (uint)partCount);
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cuda.SetParameterSize(cudaSumResidual, sizeof(uint) * 4U);
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cuda.SetFunctionBlockShape(cudaSumResidual, 64, 1, 1);
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// issue work to the GPU
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cuda.LaunchAsync(cudaEstimateResidual, partCount, nResidualTasks, cudaStream);
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cuda.LaunchAsync(cudaEstimateResidual, partCount, nResidualTasks / 4, cudaStream);
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//cuda.LaunchAsync(cudaSumResidualChunks, 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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@@ -1109,21 +1134,21 @@ namespace CUETools.Codecs.FlaCuda
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return;
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cuda.SetParameter(cudaComputeAutocor, 0, (uint)cudaAutocorOutput.Pointer);
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cuda.SetParameter(cudaComputeAutocor, IntPtr.Size, (uint)cudaSamples.Pointer);
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cuda.SetParameter(cudaComputeAutocor, IntPtr.Size * 2, (uint)cudaWindow.Pointer);
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cuda.SetParameter(cudaComputeAutocor, IntPtr.Size * 3, (uint)cudaAutocorTasks.Pointer);
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cuda.SetParameter(cudaComputeAutocor, IntPtr.Size * 4, (uint)max_order);
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cuda.SetParameter(cudaComputeAutocor, IntPtr.Size * 4 + sizeof(uint), (uint)frame.blocksize);
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cuda.SetParameter(cudaComputeAutocor, IntPtr.Size * 4 + sizeof(uint) * 2, (uint)partSize);
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cuda.SetParameterSize(cudaComputeAutocor, (uint)(IntPtr.Size * 4) + sizeof(uint) * 3);
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cuda.SetParameter(cudaComputeAutocor, sizeof(uint), (uint)cudaSamples.Pointer);
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cuda.SetParameter(cudaComputeAutocor, sizeof(uint) * 2, (uint)cudaWindow.Pointer);
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cuda.SetParameter(cudaComputeAutocor, sizeof(uint) * 3, (uint)cudaAutocorTasks.Pointer);
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cuda.SetParameter(cudaComputeAutocor, sizeof(uint) * 4, (uint)max_order);
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cuda.SetParameter(cudaComputeAutocor, sizeof(uint) * 4 + sizeof(uint), (uint)frame.blocksize);
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cuda.SetParameter(cudaComputeAutocor, sizeof(uint) * 4 + sizeof(uint) * 2, (uint)partSize);
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cuda.SetParameterSize(cudaComputeAutocor, (uint)(sizeof(uint) * 4) + sizeof(uint) * 3);
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cuda.SetFunctionBlockShape(cudaComputeAutocor, autocorThreads, 1, 1);
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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.SetParameter(cudaComputeLPC, sizeof(uint), (uint)cudaAutocorOutput.Pointer);
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cuda.SetParameter(cudaComputeLPC, sizeof(uint) * 2, (uint)cudaAutocorTasks.Pointer);
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cuda.SetParameter(cudaComputeLPC, sizeof(uint) * 3, (uint)max_order);
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cuda.SetParameter(cudaComputeLPC, sizeof(uint) * 3 + sizeof(uint), (uint)partCount);
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cuda.SetParameterSize(cudaComputeLPC, (uint)(sizeof(uint) * 3) + sizeof(uint) * 2);
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cuda.SetFunctionBlockShape(cudaComputeLPC, 64, 1, 1);
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// issue work to the GPU
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@@ -1268,24 +1293,30 @@ namespace CUETools.Codecs.FlaCuda
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if (!inited)
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{
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cuda = new CUDA(true, InitializationFlags.None);
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cuda.CreateContext(0, CUCtxFlags.SchedSpin);
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cuda.LoadModule(System.IO.Path.Combine(Environment.CurrentDirectory, "flacuda.cubin"));
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cuda.CreateContext(0, CUCtxFlags.BlockingSync);
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using (Stream cubin = GetType().Assembly.GetManifestResourceStream(GetType(), "flacuda.cubin"))
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using (StreamReader sr = new StreamReader(cubin))
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cuda.LoadModule(new ASCIIEncoding().GetBytes(sr.ReadToEnd()));
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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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cudaSumResidualChunks = cuda.GetModuleFunction("cudaSumResidualChunks");
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cudaEncodeResidual = cuda.GetModuleFunction("cudaEncodeResidual");
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cudaSamples = cuda.Allocate((uint)(sizeof(int) * FlaCudaWriter.MAX_BLOCKSIZE * (channels == 2 ? 4 : channels)));
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cudaWindow = cuda.Allocate((uint)sizeof(float) * FlaCudaWriter.MAX_BLOCKSIZE * 2 * lpc.MAX_LPC_WINDOWS);
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cudaAutocorTasks = cuda.Allocate((uint)(sizeof(computeAutocorTaskStruct) * (channels == 2 ? 4 : channels) * lpc.MAX_LPC_WINDOWS));
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cudaAutocorOutput = cuda.Allocate((uint)(sizeof(float) * (lpc.MAX_LPC_ORDER + 1) * (channels == 2 ? 4 : channels) * lpc.MAX_LPC_WINDOWS) * maxAutocorParts);
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cudaResidualTasks = cuda.Allocate((uint)(sizeof(encodeResidualTaskStruct) * (channels == 2 ? 4 : channels) * (lpc.MAX_LPC_ORDER * lpc.MAX_LPC_WINDOWS + 4)));
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cudaResidualOutput = cuda.Allocate((uint)(sizeof(int) * (channels == 2 ? 4 : channels) * (lpc.MAX_LPC_ORDER + 1) * lpc.MAX_LPC_WINDOWS * maxResidualParts));
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cudaResidualOutput = cuda.Allocate((uint)(sizeof(int) * FlaCudaWriter.MAX_BLOCKSIZE * (channels == 2 ? 4 : channels) * (lpc.MAX_LPC_ORDER * lpc.MAX_LPC_WINDOWS + 4)));
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cudaResidualSums = cuda.Allocate((uint)(sizeof(int) * (channels == 2 ? 4 : channels) * (lpc.MAX_LPC_ORDER * lpc.MAX_LPC_WINDOWS + 4) * maxResidualParts));
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//cudaResidualOutput = cuda.Allocate((uint)(sizeof(int) * (channels == 2 ? 4 : channels) * (lpc.MAX_LPC_ORDER * lpc.MAX_LPC_WINDOWS + 4) * maxResidualParts));
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CUResult cuErr = CUDADriver.cuMemAllocHost(ref samplesBufferPtr, (uint)(sizeof(int) * (channels == 2 ? 4 : channels) * FlaCudaWriter.MAX_BLOCKSIZE));
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if (cuErr == CUResult.Success)
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cuErr = CUDADriver.cuMemAllocHost(ref autocorTasksPtr, (uint)(sizeof(computeAutocorTaskStruct) * (channels == 2 ? 4 : channels) * lpc.MAX_LPC_WINDOWS));
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if (cuErr == CUResult.Success)
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cuErr = CUDADriver.cuMemAllocHost(ref residualTasksPtr, (uint)(sizeof(encodeResidualTaskStruct) * (channels == 2 ? 4 : channels) * (lpc.MAX_LPC_WINDOWS * lpc.MAX_LPC_ORDER + 4)));
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cuErr = CUDADriver.cuMemAllocHost(ref residualTasksPtr, (uint)(sizeof(encodeResidualTaskStruct) * (channels == 2 ? 4 : channels) * (lpc.MAX_LPC_WINDOWS * lpc.MAX_LPC_ORDER + 8)));
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if (cuErr != CUResult.Success)
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{
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if (samplesBufferPtr != IntPtr.Zero) CUDADriver.cuMemFreeHost(samplesBufferPtr); samplesBufferPtr = IntPtr.Zero;
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@@ -1678,7 +1709,7 @@ namespace CUETools.Codecs.FlaCuda
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case 0:
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do_midside = false;
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window_function = WindowFunction.Bartlett;
|
||||
max_prediction_order = 7;
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max_prediction_order = 8;
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||||
max_partition_order = 4;
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||||
break;
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case 1:
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@@ -1694,7 +1725,7 @@ namespace CUETools.Codecs.FlaCuda
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break;
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||||
case 3:
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window_function = WindowFunction.Bartlett;
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max_prediction_order = 7;
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max_prediction_order = 8;
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break;
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case 4:
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||||
window_function = WindowFunction.Bartlett;
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@@ -1704,7 +1735,7 @@ namespace CUETools.Codecs.FlaCuda
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window_function = WindowFunction.Bartlett;
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||||
break;
|
||||
case 6:
|
||||
max_prediction_order = 10;
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//max_prediction_order = 10;
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break;
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||||
case 7:
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||||
break;
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||||
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||||
@@ -155,6 +155,7 @@ extern "C" __global__ void cudaComputeLPC(
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if (tid < 32)
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{
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||||
int precision = 13;
|
||||
int taskNo = (blockIdx.x + blockIdx.y * gridDim.x) * ((max_order + 7) & ~7) + order;
|
||||
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]);
|
||||
@@ -164,9 +165,9 @@ extern "C" __global__ void cudaComputeLPC(
|
||||
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;
|
||||
output[taskNo].coefs[tid] = coef;
|
||||
if (tid == 0)
|
||||
output[(blockIdx.x + blockIdx.y * gridDim.x) * max_order + order].shift = sh;
|
||||
output[taskNo].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]);
|
||||
@@ -175,51 +176,111 @@ extern "C" __global__ void cudaComputeLPC(
|
||||
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;
|
||||
output[taskNo].cbits = cbits;
|
||||
}
|
||||
__syncthreads();
|
||||
}
|
||||
}
|
||||
|
||||
// blockDim.x == 32
|
||||
// blockDim.y == 8
|
||||
extern "C" __global__ void cudaEstimateResidual(
|
||||
int*output,
|
||||
int*samples,
|
||||
encodeResidualTaskStruct *tasks,
|
||||
int max_order,
|
||||
int frameSize,
|
||||
int partSize // should be <= blockDim - max_order
|
||||
int partSize // should be 224
|
||||
)
|
||||
{
|
||||
__shared__ struct {
|
||||
int data[256];
|
||||
int residual[256];
|
||||
int rice[32];
|
||||
encodeResidualTaskStruct task;
|
||||
int rice[256];
|
||||
int sums[8];
|
||||
encodeResidualTaskStruct task[8];
|
||||
} shared;
|
||||
const int tid = threadIdx.x;
|
||||
// fetch task data
|
||||
if (tid < sizeof(encodeResidualTaskStruct) / sizeof(int))
|
||||
((int*)&shared.task)[tid] = ((int*)(tasks + blockIdx.y))[tid];
|
||||
const int tid = threadIdx.x + threadIdx.y * blockDim.x;
|
||||
// fetch task data (8 * 64 == 512 elements);
|
||||
((int*)&shared.task)[tid] = ((int*)(tasks + blockIdx.y * blockDim.y))[tid];
|
||||
((int*)&shared.task)[tid + 256] = ((int*)(tasks + blockIdx.y * blockDim.y))[tid + 256];
|
||||
__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;
|
||||
const int residualOrder = shared.task[threadIdx.y].residualOrder;
|
||||
const int partNumber = blockIdx.x;
|
||||
const int pos = partNumber * partSize;
|
||||
const int dataLen = min(frameSize - pos, partSize + max_order) * (residualOrder != 0);
|
||||
|
||||
// fetch samples
|
||||
shared.data[tid] = (tid < dataLen ? samples[shared.task.samplesOffs + pos + tid] : 0);
|
||||
shared.data[tid] = (tid < dataLen ? samples[shared.task[0].samplesOffs + pos + tid] : 0);
|
||||
if (tid < blockDim.y) shared.sums[tid] = 0;
|
||||
|
||||
// set upper residuals to zero, in case blockDim < 256
|
||||
//shared.residual[255 - tid] = 0;
|
||||
|
||||
const int residualLen = min(frameSize - pos - residualOrder, partSize) * (residualOrder != 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));
|
||||
if (threadIdx.x < residualOrder) shared.task[threadIdx.y].coefs[threadIdx.x] = shared.task[threadIdx.y].coefs[residualOrder - 1 - threadIdx.x];
|
||||
|
||||
__syncthreads();
|
||||
|
||||
for (int i = 0; i < residualLen; i += blockDim.x)
|
||||
{
|
||||
// compute residual
|
||||
long sum = 0;
|
||||
for (int c = 0; c < residualOrder; c++)
|
||||
sum += __mul24(shared.data[i + threadIdx.x + c], shared.task[threadIdx.y].coefs[c]);
|
||||
int res = shared.data[i + threadIdx.x + residualOrder] - (sum >> shared.task[threadIdx.y].shift);
|
||||
shared.residual[tid] = __mul24(i + threadIdx.x < residualLen, (2 * res) ^ (res >> 31));
|
||||
__syncthreads(); if (threadIdx.x < 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];
|
||||
if (threadIdx.x == 0) shared.sums[threadIdx.y] += shared.residual[tid] + shared.residual[tid + 1];
|
||||
}
|
||||
|
||||
// rice parameter search
|
||||
shared.rice[tid] = __mul24(threadIdx.x >= 15, 0x7fffff) + residualLen * (threadIdx.x + 1) + ((shared.sums[threadIdx.y] - (residualLen >> 1)) >> threadIdx.x);
|
||||
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]);
|
||||
if (threadIdx.x == 0 && residualOrder != 0)
|
||||
output[(blockIdx.y * blockDim.y + threadIdx.y) * gridDim.x + blockIdx.x] = min(shared.rice[tid], shared.rice[tid + 1]);
|
||||
}
|
||||
|
||||
// blockDim.x == 256
|
||||
// gridDim.x = frameSize / chunkSize
|
||||
extern "C" __global__ void cudaSumResidualChunks(
|
||||
int *output,
|
||||
encodeResidualTaskStruct *tasks,
|
||||
int *residual,
|
||||
int frameSize,
|
||||
int chunkSize // <= blockDim.x(256)
|
||||
)
|
||||
{
|
||||
__shared__ struct {
|
||||
int residual[256];
|
||||
int rice[32];
|
||||
} shared;
|
||||
|
||||
// fetch parameters
|
||||
const int tid = threadIdx.x;
|
||||
const int residualOrder = tasks[blockIdx.y].residualOrder;
|
||||
const int chunkNumber = blockIdx.x;
|
||||
const int pos = chunkNumber * chunkSize;
|
||||
const int residualLen = min(frameSize - pos - residualOrder, chunkSize);
|
||||
|
||||
// set upper residuals to zero, in case blockDim < 256
|
||||
shared.residual[255 - tid] = 0;
|
||||
|
||||
// read residual
|
||||
int res = (tid < residualLen) ? residual[blockIdx.y * 8192 + pos + tid] : 0;
|
||||
|
||||
// convert to unsigned
|
||||
shared.residual[tid] = (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();
|
||||
@@ -229,7 +290,6 @@ extern "C" __global__ void cudaEstimateResidual(
|
||||
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)
|
||||
{
|
||||
@@ -240,6 +300,8 @@ extern "C" __global__ void cudaEstimateResidual(
|
||||
shared.rice[tid] = min(shared.rice[tid], shared.rice[tid + 2]);
|
||||
shared.rice[tid] = min(shared.rice[tid], shared.rice[tid + 1]);
|
||||
}
|
||||
|
||||
// write output
|
||||
if (tid == 0)
|
||||
output[blockIdx.x + blockIdx.y * gridDim.x] = shared.rice[0];
|
||||
}
|
||||
@@ -247,34 +309,32 @@ extern "C" __global__ void cudaEstimateResidual(
|
||||
extern "C" __global__ void cudaSumResidual(
|
||||
encodeResidualTaskStruct *tasks,
|
||||
int *residual,
|
||||
int partCount // <= blockDim.y (64)
|
||||
int partSize,
|
||||
int partCount // <= blockDim.y (256)
|
||||
)
|
||||
{
|
||||
__shared__ struct {
|
||||
int partLen[64];
|
||||
//encodeResidualTaskStruct task;
|
||||
int partLen[256];
|
||||
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();
|
||||
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 < 128) shared.partLen[tid] += shared.partLen[tid + 128]; __syncthreads();
|
||||
//if (tid < 64) shared.partLen[tid] += shared.partLen[tid + 64]; __syncthreads();
|
||||
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];
|
||||
@@ -288,58 +348,6 @@ extern "C" __global__ void cudaEncodeResidual(
|
||||
int partSize // should be <= blockDim - max_order
|
||||
)
|
||||
{
|
||||
__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];
|
||||
}
|
||||
#endif
|
||||
|
||||
Reference in New Issue
Block a user