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a bit more precision

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This commit is contained in:
chudov
2009-09-14 09:19:22 +00:00
parent fa4b1ef15d
commit f18df9fb9d
3 changed files with 198 additions and 164 deletions
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2
CUETools.FlaCuda/FlaCudaWriter.cs
View File

@@ -1171,7 +1171,7 @@ namespace CUETools.Codecs.FlaCuda
cuda.SetParameter(cudaComputeLPC, sizeof(uint) * 3, (uint)max_order); cuda.SetParameter(cudaComputeLPC, sizeof(uint) * 3, (uint)max_order);
cuda.SetParameter(cudaComputeLPC, sizeof(uint) * 3 + sizeof(uint), (uint)partCount); cuda.SetParameter(cudaComputeLPC, sizeof(uint) * 3 + sizeof(uint), (uint)partCount);
cuda.SetParameterSize(cudaComputeLPC, (uint)(sizeof(uint) * 3) + sizeof(uint) * 2); cuda.SetParameterSize(cudaComputeLPC, (uint)(sizeof(uint) * 3) + sizeof(uint) * 2);
cuda.SetFunctionBlockShape(cudaComputeLPC, 64, 1, 1); cuda.SetFunctionBlockShape(cudaComputeLPC, (partCount + 31) & ~31, 1, 1);
// issue work to the GPU // issue work to the GPU
cuda.CopyHostToDeviceAsync(cudaSamples, samplesBufferPtr, (uint)(sizeof(int) * FlaCudaWriter.MAX_BLOCKSIZE * channelsCount), cudaStream); cuda.CopyHostToDeviceAsync(cudaSamples, samplesBufferPtr, (uint)(sizeof(int) * FlaCudaWriter.MAX_BLOCKSIZE * channelsCount), cudaStream);

33
CUETools.FlaCuda/flacuda.cu
View File

@@ -110,6 +110,7 @@ extern "C" __global__ void cudaComputeLPC(
volatile float autoc[33]; volatile float autoc[33];
volatile float gen0[32]; volatile float gen0[32];
volatile float gen1[32]; volatile float gen1[32];
volatile float parts[128];
//volatile float reff[32]; //volatile float reff[32];
int cbits; int cbits;
} shared; } shared;
@@ -119,15 +120,26 @@ extern "C" __global__ void cudaComputeLPC(
if (tid < sizeof(shared.task) / sizeof(int)) if (tid < sizeof(shared.task) / sizeof(int))
((int*)&shared.task)[tid] = ((int*)(tasks + blockIdx.y))[tid]; ((int*)&shared.task)[tid] = ((int*)(tasks + blockIdx.y))[tid];
// initialize autoc sums
if (tid <= max_order)
shared.autoc[tid] = 0.0f;
// add up parts // add up parts
for (int part = 0; part < partCount; part++) for (int order = 0; order <= max_order; order++)
if (tid <= max_order) {
shared.autoc[tid] += autoc[(blockIdx.y * partCount + part) * (max_order + 1) + tid]; shared.parts[tid] = tid < partCount ? autoc[(blockIdx.y * partCount + tid) * (max_order + 1) + order] : 0;
__syncthreads();
if (tid < 64 && blockDim.x > 64) shared.parts[tid] += shared.parts[tid + 64];
__syncthreads();
if (tid < 32)
{
if (blockDim.x > 32) shared.parts[tid] += shared.parts[tid + 32];
shared.parts[tid] += shared.parts[tid + 16];
shared.parts[tid] += shared.parts[tid + 8];
shared.parts[tid] += shared.parts[tid + 4];
shared.parts[tid] += shared.parts[tid + 2];
shared.parts[tid] += shared.parts[tid + 1];
if (tid == 0)
shared.autoc[order] = shared.parts[0];
}
}
if (tid < 32) if (tid < 32)
{ {
shared.gen0[tid] = shared.autoc[tid+1]; shared.gen0[tid] = shared.autoc[tid+1];
@@ -195,7 +207,7 @@ extern "C" __global__ void cudaEstimateResidual(
{ {
__shared__ struct { __shared__ struct {
int data[32*8]; int data[32*8];
int residual[32*8]; volatile int residual[32*8];
encodeResidualTaskStruct task[8]; encodeResidualTaskStruct task[8];
} shared; } shared;
const int tid = threadIdx.x + threadIdx.y * blockDim.x; const int tid = threadIdx.x + threadIdx.y * blockDim.x;
@@ -207,11 +219,11 @@ extern "C" __global__ void cudaEstimateResidual(
// fetch samples // fetch samples
shared.data[tid] = tid < dataLen ? samples[shared.task[0].samplesOffs + pos + tid] : 0; shared.data[tid] = tid < dataLen ? samples[shared.task[0].samplesOffs + pos + tid] : 0;
shared.residual[tid] = 0;
const int residualLen = max(0,min(frameSize - pos - shared.task[threadIdx.y].residualOrder, partSize)) * (shared.task[threadIdx.y].residualOrder != 0); const int residualLen = max(0,min(frameSize - pos - shared.task[threadIdx.y].residualOrder, partSize)) * (shared.task[threadIdx.y].residualOrder != 0);
__syncthreads(); __syncthreads();
shared.residual[tid] = 0;
shared.task[threadIdx.y].coefs[threadIdx.x] = threadIdx.x < max_order ? tasks[blockIdx.y * blockDim.y + threadIdx.y].coefs[threadIdx.x] : 0; shared.task[threadIdx.y].coefs[threadIdx.x] = threadIdx.x < max_order ? tasks[blockIdx.y * blockDim.y + threadIdx.y].coefs[threadIdx.x] : 0;
for (int i = threadIdx.x; i - threadIdx.x < residualLen; i += blockDim.x) // += 32 for (int i = threadIdx.x; i - threadIdx.x < residualLen; i += blockDim.x) // += 32
@@ -235,7 +247,6 @@ extern "C" __global__ void cudaEstimateResidual(
// rice parameter search // rice parameter search
shared.residual[tid] = __mul24(threadIdx.x >= 15, 0x7fffff) + residualLen * (threadIdx.x + 1) + ((shared.residual[threadIdx.y * blockDim.x] - (residualLen >> 1)) >> threadIdx.x); shared.residual[tid] = __mul24(threadIdx.x >= 15, 0x7fffff) + residualLen * (threadIdx.x + 1) + ((shared.residual[threadIdx.y * blockDim.x] - (residualLen >> 1)) >> threadIdx.x);
__syncthreads();
shared.residual[tid] = min(shared.residual[tid], shared.residual[tid + 8]); shared.residual[tid] = min(shared.residual[tid], shared.residual[tid + 8]);
shared.residual[tid] = min(shared.residual[tid], shared.residual[tid + 4]); shared.residual[tid] = min(shared.residual[tid], shared.residual[tid + 4]);
shared.residual[tid] = min(shared.residual[tid], shared.residual[tid + 2]); shared.residual[tid] = min(shared.residual[tid], shared.residual[tid + 2]);

327
CUETools.FlaCuda/flacuda.cubin
View File

@@ -125,55 +125,56 @@ code {
0xa000420d 0x04200780 0x30100c19 0xc4100780 0xa000420d 0x04200780 0x30100c19 0xc4100780
0x60080411 0x00018780 0x40060a05 0x00000780 0x60080411 0x00018780 0x40060a05 0x00000780
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0x60060819 0x00018780 0x2101ee0c 0x2144f004 0x60060819 0x00018780 0x2101ee20 0x2144f004
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0x861ffe03 0x00000000 0x3000cffd 0x6420c7c8 0x3000cffd 0x6420c7c8 0xa0045003 0x00000000
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0xa0076003 0x00000000 0x10000005 0x0403c780 0x08043401 0xe4204780 0xa0072003 0x00000000
0x10073003 0x00000280 0x00020e0d 0xc0000780 0x10000005 0x0403c780 0x10072003 0x00000280
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0xa0060003 0x00000000 0x1000f80d 0x0403c780 0xa005e003 0x00000000 0x1000f815 0x0403c780
0x1000f825 0x0403c780 0x10060003 0x00000280 0x1000f825 0x0403c780 0x1005e003 0x00000280
0xa005e003 0x00000000 0xd4000009 0x20000780 0xa005c003 0x00000000 0xd4000009 0x20000780
0x20000221 0x0400c780 0x0002100d 0xc0000780 0x20000221 0x04014780 0x0002100d 0xc0000780
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0x6c08d425 0x80224780 0xd410000d 0x20000780 0x6c08d425 0x80224780 0xd410000d 0x20000780
0x2001860d 0x00000003 0x3c03c1fd 0x6c2147c8 0x20018a15 0x00000003 0x3c05c1fd 0x6c2147c8
0xd8000809 0x20000780 0x10054003 0x00000280 0xd8000809 0x20000780 0x10052003 0x00000280
0xd4100009 0x20000782 0x1800c00d 0x0423c780 0xd4100009 0x20000782 0x1800c015 0x0423c780
0x2000020d 0x0400c782 0x00020609 0xc0000780 0x20000215 0x04014782 0x00020a09 0xc0000780
0xd410100d 0x20000780 0x1c00c00d 0x0423c780 0xd410100d 0x20000780 0x1c00c015 0x0423c780
0x3003120d 0xec000780 0x2840d40d 0x0420c780 0x30051215 0xec000780 0x2840d415 0x04214780
0x301f0621 0xec100780 0x3001060d 0xc4100780 0x301f0a21 0xec100780 0x30010a15 0xc4100780
0xd0031021 0x04008780 0x3001080d 0x6c010780 0xd0051021 0x04008780 0x00020e0d 0xc0000780
0xa000060d 0x2c014780 0x60080615 0x80014780 0xdc085009 0x20000780 0x30010825 0x6c010780
0xa000420d 0x04200780 0x20038204 0x2040820c 0x1800c015 0x0423c780 0xa0001225 0x2c014780
0x300309fd 0x640107c8 0x00020e0d 0xc0000780 0x60081215 0x80014780 0x20000205 0x0400c780
0x0c021401 0xe4214780 0x1004c003 0x00000280 0x0c021401 0xe4214780 0x20400215 0x04000780
0x10076003 0x00000780 0x00020e0d 0xc0000780 0x300509fd 0x640107c8 0x1004a003 0x00000280
0xdc085009 0x20000780 0x1800c015 0x0423c780 0x00020e0d 0xc0000782 0xdc085011 0x20000780
0x00020e0d 0xc0000782 0xdc085809 0x20000780 0x1000e005 0x0423c784 0x2000c005 0x04204784
0x2800de05 0x04214780 0x0c021401 0xe4204780 0x0c021401 0xe4204780 0x1000d005 0x0423c784
0x2800ce05 0x04204780 0x0c021401 0xe4204780 0x2000c005 0x04204784 0x0c021401 0xe4204780
0x2800c605 0x04204780 0x0c021401 0xe4204780 0x1000c805 0x0423c784 0x2000c005 0x04204784
0x2800c20d 0x04204780 0x20018005 0x00000003 0x0c021401 0xe4204780 0x1000c405 0x0423c784
0x0c021401 0xe420c780 0x2903e00c 0x40031014 0x2000c005 0x04204784 0x0c021401 0xe4204780
0x20018005 0x00000003 0x1000c20d 0x0423c784
0x2000c00d 0x0420c784 0x40031015 0x00000780
0x0c021401 0xe420c780 0x6002120d 0x00014780 0x0c021401 0xe420c780 0x6002120d 0x00014780
0x30820021 0x64410780 0x00020c09 0xc0000780 0x30820021 0x64410780 0x00020c09 0xc0000780
0xd8085009 0x20000780 0x30100615 0xc4100780 0xd8085009 0x20000780 0x30100615 0xc4100780
@@ -181,20 +182,21 @@ code {
0x60021011 0x00014780 0x407f8c15 0x0007ffff 0x60021011 0x00014780 0x407f8c15 0x0007ffff
0x2943e004 0x2005880c 0x30000205 0xec000780 0x2943e004 0x2005880c 0x30000205 0xec000780
0x20000205 0x0400c780 0x0c021401 0xe4204780 0x20000205 0x0400c780 0x0c021401 0xe4204780
0x861ffe03 0x00000000 0x00020e0d 0xc0000780 0x1000d005 0x0423c784 0x3001c005 0xac200784
0xdc085009 0x20000780 0x1800d005 0x0423c780 0x0c021401 0xe4204780 0x1000c805 0x0423c784
0x3801c005 0xac200780 0x0c021401 0xe4204780 0x3001c005 0xac200784 0x0c021401 0xe4204780
0x3801c805 0xac200780 0x0c021401 0xe4204780 0x1000c405 0x0423c784 0x3001c005 0xac200784
0x3801c405 0xac200780 0x0c021401 0xe4204780 0x0c021401 0xe4204780 0x1000c205 0x0423c784
0x3801c205 0xac200780 0x307c01fd 0x640147c8 0x3001c005 0xac200784 0x307c01fd 0x640147c8
0x0c021401 0xe4204780 0x30000003 0x00000280 0x0c021401 0xe4204780 0x30000003 0x00000280
0xd4100005 0x20000780 0x347cc1fd 0x6c2087c8 0xd4100005 0x20000780 0x347cc1fd 0x6c2087c8
0x30000003 0x00000280 0x10004401 0x0023c780 0x30000003 0x00000280 0x10004401 0x0023c780
0x60004e01 0x00208780 0x40014809 0x00200780 0x60004e01 0x00208780 0x40014805 0x00200780
0x30100409 0xc4100780 0x60004801 0x00208780 0x30100205 0xc4100780 0x60004801 0x00204780
0xa0004c11 0x04200780 0x20000001 0x04010780 0xa0004c11 0x04200780 0x20000001 0x04010780
0x30020001 0xc4100780 0x2000c801 0x04200780 0x00020e0d 0xc0000780 0xdc085005 0x20000780
0xd00e0005 0xa0c00781 0x30020005 0xc4100780 0x1500e000 0x2101e804
0xd00e0201 0xa0c00781
} }
} }
code { code {
@@ -278,117 +280,138 @@ code {
code { code {
name = cudaComputeLPC name = cudaComputeLPC
lmem = 0 lmem = 0
smem = 700 smem = 1212
reg = 10 reg = 10
bar = 0 bar = 1
const { const {
segname = const segname = const
segnum = 1 segnum = 1
offset = 0 offset = 0
bytes = 44 bytes = 52
mem { mem {
0x00000003 0x0000001f 0x7e800000 0x00000001 0x00000003 0x0000001f 0x0000003f 0x00000040
0x0000000f 0x00001fff 0xffffe000 0x3e800000 0x00000001 0x00000020 0x7e800000 0x0000000f
0x00000020 0x0000009e 0x00000008 0x00001fff 0xffffe000 0x3e800000 0x0000009e
0x00000008
} }
} }
bincode { bincode {
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0xa000b003 0x00000000 0x1000b003 0x00000280 0xa000b003 0x00000000 0x1000b003 0x00000280
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0x40070205 0x00018780 0x00000809 0xc0000780 0xc08a060d 0x00401680 0xc08a0205 0x00401680
0x08005a01 0xe4204780 0xd801680d 0x20000780 0x90000204 0xc001061c 0x20400a05 0x04018780
0x1c00e005 0x0423c780 0x3c01c005 0x8c200780 0xd0047005 0x20000780 0x300105fd 0x6c0187d8
0x08005a01 0xe4204780 0x1c00d005 0x0423c780 0xa0077003 0x00000000 0xe407c001 0x00200780
0x10077003 0x00001280 0x00000805 0xc0000780
0xd404780d 0x20000780 0xd4037009 0x20000780
0x1d00e004 0x1900e00c 0xe807c005 0x00204780
0xec07c00d 0x0020c780 0x04011c01 0xe4204780
0x0400dc01 0xe420c780 0x20400c05 0x04008782
0x00020205 0xc0000780 0x30020dfd 0x6c00c7d8
0xc407d80d 0x00200780 0x1000f80d 0x0403d280
0x30020dfd 0x6c0147d8 0xb0000605 0x0001c780
0x10000605 0x0403d280 0x00000809 0xc0000780
0xb800da05 0x00204780 0x08001a01 0xe4204780
0xa800da05 0xc4304780 0xc0000205 0x04700003
0xa0000205 0x8c0047d0 0x2000d60d 0x04218780
0xa0000205 0x44065680 0x30170205 0xec101680
0x31000205 0x0442d680 0x10000a05 0x2440d100
0x30020c1d 0x6c0187d0 0x30148205 0x00000003
0xd0840e1d 0x04400780 0x40070205 0x00018780
0x00000809 0xc0000780 0x08005a01 0xe4204780
0xd801680d 0x20000780 0x1c00e005 0x0423c780
0x3c01c005 0x8c200780 0x08005a01 0xe4204780 0x3c01c005 0x8c200780 0x08005a01 0xe4204780
0x1c00c805 0x0423c780 0x3c01c005 0x8c200780 0x1c00d005 0x0423c780 0x3c01c005 0x8c200780
0x08005a01 0xe4204780 0x1c00c405 0x0423c780 0x08005a01 0xe4204780 0x1c00c805 0x0423c780
0x3c01c005 0x8c200780 0x08005a01 0xe4204780 0x3c01c005 0x8c200780 0x08005a01 0xe4204780
0x1c00c205 0x0423c780 0x3c01c005 0x8c200780 0x1c00c405 0x0423c780 0x3c01c005 0x8c200780
0x08005a01 0xe4204780 0xd0016809 0x20000780 0x08005a01 0xe4204780 0x1c00c205 0x0423c780
0x390fe005 0x00000003 0x30840205 0xac400780 0x3c01c005 0x8c200780 0x08005a01 0xe4204780
0x1001801d 0x00000003 0x307c0205 0x8c000780 0xd0016809 0x20000780 0x390fe005 0x00000003
0x30010e1d 0xc4000780 0xa0000e1d 0x44014780 0x30870205 0xac400780 0x1001801d 0x00000003
0xc407da1d 0x00200780 0xa0000e1d 0xac004780 0x307c0205 0x8c000780 0x30010e1d 0xc4000780
0x30850e1d 0xac400780 0xa008c003 0x00000000 0xa0000e1d 0x44014780 0xc407da1d 0x00200780
0x30860e1d 0x8c400780 0x1008c003 0x00001100 0xa0000e1d 0xac004780 0x30880e1d 0xac400780
0x30070421 0xc4100780 0x30060425 0xc4100780 0xa00b3003 0x00000000 0x30890e1d 0x8c400780
0x20099020 0x2108e820 0x20000821 0x04020780 0x100b3003 0x00001100 0x30070621 0xc4100780
0x20009021 0x00000007 0xd00e101d 0xa0c00780 0x30060625 0xc4100780 0x20099020 0x2108e820
0xf0000001 0xe0000002 0x30070421 0xc4100680 0x20000821 0x04020780 0x20009021 0x00000007
0x30060425 0xc4100680 0x20001021 0x04024680 0xd00e101d 0xa0c00780 0xf0000001 0xe0000002
0x2000c821 0x04220680 0x21001021 0x04428680 0x30070621 0xc4100680 0x30060625 0xc4100680
0xd00e1005 0xa0c00680 0x307c0ffd 0x6c0087d8 0x20001021 0x04024680 0x2000c821 0x04220680
0xa0000e05 0x44065500 0x30170205 0xec101500 0x21001021 0x04430680 0xd00e1005 0xa0c00680
0x31000205 0x04425500 0x10001005 0x2440d280 0x307c0ffd 0x6c0087d8 0xa0000e05 0x44065500
0xd007001d 0x0402c780 0x307c0ffd 0x6c0087d8 0x30170205 0xec101500 0x31000205 0x0442d500
0xa0000e1d 0x44065500 0x30170e1d 0xec101500 0x10000a05 0x2440d280 0xd007001d 0x0402c780
0x31000e1d 0x04425500 0x1000101d 0x2440d280 0x307c0ffd 0x6c0087d8 0xa0000e1d 0x44065500
0x30070205 0x8c000780 0x00000805 0xc0000780 0x30170e1d 0xec101500 0x31000e1d 0x0442d500
0x30218205 0x00000003 0x04005a01 0xe4204780 0x10000a1d 0x2440d280 0x30070205 0x8c000780
0xd4016809 0x20000780 0x1800e005 0x0423c780 0x00000805 0xc0000780 0x30218205 0x00000003
0x04005a01 0xe4204780 0xd4016809 0x20000780
0x1800e005 0x0423c780 0x3801c005 0x8c200780
0x04005a01 0xe4204780 0x1800d005 0x0423c780
0x3801c005 0x8c200780 0x04005a01 0xe4204780 0x3801c005 0x8c200780 0x04005a01 0xe4204780
0x1800d005 0x0423c780 0x3801c005 0x8c200780 0x1800c805 0x0423c780 0x3801c005 0x8c200780
0x04005a01 0xe4204780 0x1800c805 0x0423c780 0x04005a01 0xe4204780 0x1800c405 0x0423c780
0x3801c005 0x8c200780 0x04005a01 0xe4204780 0x3801c005 0x8c200780 0x04005a01 0xe4204780
0x1800c405 0x0423c780 0x3801c005 0x8c200780 0x1800c205 0x0423c780 0x3801c005 0x8c200780
0x04005a01 0xe4204780 0x1800c205 0x0423c780 0x04005a01 0xe4204780 0xa00e2003 0x00000000
0x3801c005 0x8c200780 0x04005a01 0xe4204780 0x100e2003 0x00000100 0x30070605 0xc4100780
0xa00bb003 0x00000000 0x100bb003 0x00000100 0x3006060d 0xc4100780 0x20000205 0x0400c780
0x30070405 0xc4100780 0x30060409 0xc4100780 0xd0016805 0x20000780 0x2101e80c 0x1500e004
0x20000205 0x04008780 0xd0016805 0x20000780 0x200c860d 0x00000003 0xd00e0605 0xa0c00780
0x2101e808 0x1500e004 0x200c8409 0x00000003 0xf0000001 0xe0000002 0x20018c19 0x00000003
0xd00e0405 0xa0c00780 0xf0000001 0xe0000002 0x3006cffd 0x6c2147d8 0x10062003 0x00001280
0x20018c19 0x00000003 0x3006cffd 0x6c2147d8 0xf0000001 0xe0000001
0x1003b003 0x00001280 0xf0000001 0xe0000001
} }
} }
code { code {