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trying to do rice partitioning on gpu

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chudov
2009-10-05 11:53:33 +00:00
parent d96ba46ad3
commit 2bfcac5f49
3 changed files with 396 additions and 319 deletions
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10
CUETools.FlaCuda/FlaCudaWriter.cs
View File

@@ -1114,7 +1114,7 @@ namespace CUETools.Codecs.FlaCuda
int calcPartitionPartCount = (calcPartitionPartSize >= 128) ? 1 : (256 / calcPartitionPartSize); int calcPartitionPartCount = (calcPartitionPartSize >= 128) ? 1 : (256 / calcPartitionPartSize);
CUfunction cudaChannelDecorr = channels == 2 ? (channelsCount == 4 ? task.cudaStereoDecorr : task.cudaChannelDecorr2) : task.cudaChannelDecorr; CUfunction cudaChannelDecorr = channels == 2 ? (channelsCount == 4 ? task.cudaStereoDecorr : task.cudaChannelDecorr2) : task.cudaChannelDecorr;
CUfunction cudaCalcPartition = calcPartitionPartSize >= 128 ? task.cudaCalcLargePartition : task.cudaCalcPartition; CUfunction cudaCalcPartition = calcPartitionPartSize >= 128 ? task.cudaCalcLargePartition : calcPartitionPartSize == 16 && task.frameSize >= 256 ? task.cudaCalcPartition16 : task.cudaCalcPartition;
cuda.SetParameter(cudaChannelDecorr, 0 * sizeof(uint), (uint)task.cudaSamples.Pointer); cuda.SetParameter(cudaChannelDecorr, 0 * sizeof(uint), (uint)task.cudaSamples.Pointer);
cuda.SetParameter(cudaChannelDecorr, 1 * sizeof(uint), (uint)task.cudaSamplesBytes.Pointer); cuda.SetParameter(cudaChannelDecorr, 1 * sizeof(uint), (uint)task.cudaSamplesBytes.Pointer);
@@ -1203,13 +1203,13 @@ namespace CUETools.Codecs.FlaCuda
cuda.SetParameter(task.cudaSumPartition, 0, (uint)task.cudaPartitions.Pointer); cuda.SetParameter(task.cudaSumPartition, 0, (uint)task.cudaPartitions.Pointer);
cuda.SetParameter(task.cudaSumPartition, 1 * sizeof(uint), (uint)max_porder); cuda.SetParameter(task.cudaSumPartition, 1 * sizeof(uint), (uint)max_porder);
cuda.SetParameterSize(task.cudaSumPartition, 2U * sizeof(uint)); cuda.SetParameterSize(task.cudaSumPartition, 2U * sizeof(uint));
cuda.SetFunctionBlockShape(task.cudaSumPartition, Math.Max(64, 1 << max_porder), 1, 1); cuda.SetFunctionBlockShape(task.cudaSumPartition, Math.Max(32, 1 << (max_porder - 1)), 1, 1);
cuda.SetParameter(task.cudaFindRiceParameter, 0, (uint)task.cudaRiceParams.Pointer); cuda.SetParameter(task.cudaFindRiceParameter, 0, (uint)task.cudaRiceParams.Pointer);
cuda.SetParameter(task.cudaFindRiceParameter, 1 * sizeof(uint), (uint)task.cudaPartitions.Pointer); cuda.SetParameter(task.cudaFindRiceParameter, 1 * sizeof(uint), (uint)task.cudaPartitions.Pointer);
cuda.SetParameter(task.cudaFindRiceParameter, 2 * sizeof(uint), (uint)max_porder); cuda.SetParameter(task.cudaFindRiceParameter, 2 * sizeof(uint), (uint)max_porder);
cuda.SetParameterSize(task.cudaFindRiceParameter, 3U * sizeof(uint)); cuda.SetParameterSize(task.cudaFindRiceParameter, 3U * sizeof(uint));
cuda.SetFunctionBlockShape(task.cudaFindRiceParameter, 8, 32, 1); cuda.SetFunctionBlockShape(task.cudaFindRiceParameter, 32, 8, 1);
cuda.SetParameter(task.cudaFindPartitionOrder, 0, (uint)task.cudaBestRiceParams.Pointer); cuda.SetParameter(task.cudaFindPartitionOrder, 0, (uint)task.cudaBestRiceParams.Pointer);
cuda.SetParameter(task.cudaFindPartitionOrder, 1 * sizeof(uint), (uint)task.cudaBestResidualTasks.Pointer); cuda.SetParameter(task.cudaFindPartitionOrder, 1 * sizeof(uint), (uint)task.cudaBestResidualTasks.Pointer);
@@ -1238,7 +1238,7 @@ namespace CUETools.Codecs.FlaCuda
if (!encode_on_cpu) if (!encode_on_cpu)
{ {
int bsz = calcPartitionPartCount * calcPartitionPartSize; int bsz = calcPartitionPartCount * calcPartitionPartSize;
if (cudaCalcPartition.Pointer != task.cudaCalcPartition.Pointer) if (cudaCalcPartition.Pointer == task.cudaCalcLargePartition.Pointer)
cuda.LaunchAsync(task.cudaEncodeResidual, residualPartCount, channels * task.frameCount, task.stream); cuda.LaunchAsync(task.cudaEncodeResidual, residualPartCount, channels * task.frameCount, task.stream);
cuda.LaunchAsync(cudaCalcPartition, (task.frameSize + bsz - 1) / bsz, channels * task.frameCount, task.stream); cuda.LaunchAsync(cudaCalcPartition, (task.frameSize + bsz - 1) / bsz, channels * task.frameCount, task.stream);
if (max_porder > 0) if (max_porder > 0)
@@ -1936,6 +1936,7 @@ namespace CUETools.Codecs.FlaCuda
public CUfunction cudaCopyBestMethodStereo; public CUfunction cudaCopyBestMethodStereo;
public CUfunction cudaEncodeResidual; public CUfunction cudaEncodeResidual;
public CUfunction cudaCalcPartition; public CUfunction cudaCalcPartition;
public CUfunction cudaCalcPartition16;
public CUfunction cudaCalcLargePartition; public CUfunction cudaCalcLargePartition;
public CUfunction cudaSumPartition; public CUfunction cudaSumPartition;
public CUfunction cudaFindRiceParameter; public CUfunction cudaFindRiceParameter;
@@ -2035,6 +2036,7 @@ namespace CUETools.Codecs.FlaCuda
cudaCopyBestMethodStereo = cuda.GetModuleFunction("cudaCopyBestMethodStereo"); cudaCopyBestMethodStereo = cuda.GetModuleFunction("cudaCopyBestMethodStereo");
cudaEncodeResidual = cuda.GetModuleFunction("cudaEncodeResidual"); cudaEncodeResidual = cuda.GetModuleFunction("cudaEncodeResidual");
cudaCalcPartition = cuda.GetModuleFunction("cudaCalcPartition"); cudaCalcPartition = cuda.GetModuleFunction("cudaCalcPartition");
cudaCalcPartition16 = cuda.GetModuleFunction("cudaCalcPartition16");
cudaCalcLargePartition = cuda.GetModuleFunction("cudaCalcLargePartition"); cudaCalcLargePartition = cuda.GetModuleFunction("cudaCalcLargePartition");
cudaSumPartition = cuda.GetModuleFunction("cudaSumPartition"); cudaSumPartition = cuda.GetModuleFunction("cudaSumPartition");
cudaFindRiceParameter = cuda.GetModuleFunction("cudaFindRiceParameter"); cudaFindRiceParameter = cuda.GetModuleFunction("cudaFindRiceParameter");

169
CUETools.FlaCuda/flacuda.cu
View File

@@ -859,42 +859,51 @@ extern "C" __global__ void cudaCalcPartition(
else else
s = 0; s = 0;
__syncthreads();
// convert to unsigned // convert to unsigned
shared.data[tid] = min(0xfffff, (s << 1) ^ (s >> 31)); s = min(0xfffff, (s << 1) ^ (s >> 31));
//__syncthreads();
//shared.data[tid] = s;
//__syncthreads();
//shared.data[tid] = (shared.data[tid] & (0x0000ffff << (tid & 16))) | (((shared.data[tid ^ 16] & (0x0000ffff << (tid & 16))) << (~tid & 16)) >> (tid & 16));
//shared.data[tid] = (shared.data[tid] & (0x00ff00ff << (tid & 8))) | (((shared.data[tid ^ 8] & (0x00ff00ff << (tid & 8))) << (~tid & 8)) >> (tid & 8));
//shared.data[tid] = (shared.data[tid] & (0x0f0f0f0f << (tid & 4))) | (((shared.data[tid ^ 4] & (0x0f0f0f0f << (tid & 4))) << (~tid & 4)) >> (tid & 4));
//shared.data[tid] = (shared.data[tid] & (0x33333333 << (tid & 2))) | (((shared.data[tid ^ 2] & (0x33333333 << (tid & 2))) << (~tid & 2)) >> (tid & 2));
//shared.data[tid] = (shared.data[tid] & (0x55555555 << (tid & 1))) | (((shared.data[tid ^ 1] & (0x55555555 << (tid & 1))) << (~tid & 1)) >> (tid & 1));
//shared.data[tid] = __popc(shared.data[tid]);
__syncthreads();
shared.data[tid + (tid / psize)] = s;
//shared.data[tid] = s;
__syncthreads(); __syncthreads();
s = (psize - shared.task.residualOrder * (threadIdx.y + blockIdx.x == 0)) * (threadIdx.x + 1); s = (psize - shared.task.residualOrder * (threadIdx.x + blockIdx.x == 0)) * (threadIdx.y + 1);
int dpos = threadIdx.y * psize; int dpos = __mul24(threadIdx.x, psize + 1);
// calc number of unary bits for each residual part with each rice paramater //int dpos = __mul24(threadIdx.x, psize);
// calc number of unary bits for part threadIdx.x with rice paramater threadIdx.y
#pragma unroll 0 #pragma unroll 0
for (int i = 0; i < psize; i++) for (int i = 0; i < psize; i++)
// for part (threadIdx.y) with this rice paramater (threadIdx.x) s += shared.data[dpos + i] >> threadIdx.y;
s += shared.data[dpos + i] >> threadIdx.x;
__syncthreads();
shared.data[tid] = s;
__syncthreads();
// output length (transposed: k is now threadIdx.y) // output length
const int pos = (15 << (max_porder + 1)) * blockIdx.y + (threadIdx.y << (max_porder + 1)); const int pos = (15 << (max_porder + 1)) * blockIdx.y + (threadIdx.y << (max_porder + 1));
if (threadIdx.y <= 14 && threadIdx.x < parts) if (threadIdx.y <= 14 && threadIdx.x < parts)
partition_lengths[pos + blockIdx.x * parts_per_block + threadIdx.x] = shared.data[threadIdx.y + (threadIdx.x << 4)]; partition_lengths[pos + blockIdx.x * parts_per_block + threadIdx.x] = s;
} }
extern "C" __global__ void cudaCalcPartition1( extern "C" __global__ void cudaCalcPartition16(
int* partition_lengths, int* partition_lengths,
int* residual, int* residual,
int* samples, int* samples,
encodeResidualTaskStruct *tasks, encodeResidualTaskStruct *tasks,
int max_porder, // <= 8 int max_porder, // <= 8
int psize, // == (shared.task.blocksize >> max_porder), < 256 int psize, // == 16
int parts_per_block // == 256 / psize, > 0, <= 16 int parts_per_block // == 16
) )
{ {
__shared__ struct { __shared__ struct {
int data[256]; int data[256+32];
int length[256];
int plen[256];
encodeResidualTaskStruct task; encodeResidualTaskStruct task;
} shared; } shared;
const int tid = threadIdx.x + (threadIdx.y << 4); const int tid = threadIdx.x + (threadIdx.y << 4);
@@ -902,32 +911,46 @@ extern "C" __global__ void cudaCalcPartition1(
((int*)&shared.task)[tid] = ((int*)(&tasks[blockIdx.y]))[tid]; ((int*)&shared.task)[tid] = ((int*)(&tasks[blockIdx.y]))[tid];
__syncthreads(); __syncthreads();
const int parts = min(parts_per_block, (1 << max_porder) - blockIdx.x * parts_per_block); const int offs = (blockIdx.x << 8) + tid;
// fetch samples
if (tid < 32) shared.data[tid] = min(offs, tid + shared.task.residualOrder) >= 32 ? samples[shared.task.samplesOffs + offs - 32] >> shared.task.wbits : 0;
shared.data[32 + tid] = samples[shared.task.samplesOffs + offs] >> shared.task.wbits;
__syncthreads();
// compute residual
int s = 0;
for (int c = -shared.task.residualOrder; c < 0; c++)
s += __mul24(shared.data[32 + tid + c], shared.task.coefs[shared.task.residualOrder + c]);
s = shared.data[32 + tid] - (s >> shared.task.shift);
if (offs >= shared.task.residualOrder)
residual[shared.task.residualOffs + offs] = s;
else
s = 0;
// fetch residual
int offs = blockIdx.x * psize * parts_per_block + tid;
int s = (offs >= shared.task.residualOrder && tid < parts * psize) ? residual[shared.task.residualOffs + offs] : 0;
// convert to unsigned // convert to unsigned
shared.data[tid] = min(0xfffff, (s << 1) ^ (s >> 31)); s = min(0xfffff, (s << 1) ^ (s >> 31));
__syncthreads();
shared.data[tid + threadIdx.y] = s;
__syncthreads(); __syncthreads();
for (int k = 0; k < 15; k++) // calc number of unary bits for part threadIdx.x with rice paramater threadIdx.y
{ int dpos = __mul24(threadIdx.x, 17);
shared.length[tid] = 0; s =
// calc number of unary bits for each residual part with each rice paramater (shared.data[dpos + 0] >> threadIdx.y) + (shared.data[dpos + 1] >> threadIdx.y) +
// for part (threadIdx.y) with rice paramater k (shared.data[dpos + 2] >> threadIdx.y) + (shared.data[dpos + 3] >> threadIdx.y) +
for (int i = 0; i < psize; i += 16) (shared.data[dpos + 4] >> threadIdx.y) + (shared.data[dpos + 5] >> threadIdx.y) +
shared.length[tid] += shared.data[threadIdx.y * psize + i + threadIdx.x] >> k; // * (i + threadIdx.x < psize) (shared.data[dpos + 6] >> threadIdx.y) + (shared.data[dpos + 7] >> threadIdx.y) +
SUM16(shared.length,tid,+=); (shared.data[dpos + 8] >> threadIdx.y) + (shared.data[dpos + 9] >> threadIdx.y) +
if (threadIdx.x == 0 && threadIdx.y < parts) (shared.data[dpos + 10] >> threadIdx.y) + (shared.data[dpos + 11] >> threadIdx.y) +
shared.plen[(k << 4) + threadIdx.y] = shared.length[tid]; (shared.data[dpos + 12] >> threadIdx.y) + (shared.data[dpos + 13] >> threadIdx.y) +
} (shared.data[dpos + 14] >> threadIdx.y) + (shared.data[dpos + 15] >> threadIdx.y);
__syncthreads();
// output length // output length
const int pos = blockIdx.x * parts_per_block + threadIdx.x; const int pos = ((15 * blockIdx.y + threadIdx.y) << (max_porder + 1)) + (blockIdx.x << 4) + threadIdx.x;
const int len1 = (psize - shared.task.residualOrder * (pos == 0)) * (threadIdx.y + 1); if (threadIdx.y <= 14)
if (threadIdx.y <= 14 && threadIdx.x < parts) partition_lengths[pos] = s + (16 - shared.task.residualOrder * (threadIdx.x + blockIdx.x == 0)) * (threadIdx.y + 1);
partition_lengths[((threadIdx.y + 15 * blockIdx.y) << (max_porder + 1)) + pos] = shared.plen[tid] + len1;
} }
extern "C" __global__ void cudaCalcLargePartition( extern "C" __global__ void cudaCalcLargePartition(
@@ -977,30 +1000,45 @@ extern "C" __global__ void cudaCalcLargePartition(
} }
// Sums partition lengths for a certain k == blockIdx.x // Sums partition lengths for a certain k == blockIdx.x
// Requires 256 threads // Requires 128 threads
extern "C" __global__ void cudaSumPartition( extern "C" __global__ void cudaSumPartition(
int* partition_lengths, int* partition_lengths,
int max_porder int max_porder
) )
{ {
__shared__ struct { __shared__ struct {
int data[512]; // max_porder <= 8, data length <= 1 << 9. volatile int data[512+32]; // max_porder <= 8, data length <= 1 << 9.
} shared; } shared;
const int pos = (15 << (max_porder + 1)) * blockIdx.y + (blockIdx.x << (max_porder + 1)); const int pos = (15 << (max_porder + 1)) * blockIdx.y + (blockIdx.x << (max_porder + 1));
// fetch partition lengths // fetch partition lengths
shared.data[threadIdx.x] = threadIdx.x < (1 << max_porder) ? partition_lengths[pos + threadIdx.x] : 0; shared.data[threadIdx.x] = threadIdx.x < (1 << max_porder) ? partition_lengths[pos + threadIdx.x] : 0;
shared.data[blockDim.x + threadIdx.x] = blockDim.x + threadIdx.x < (1 << max_porder) ? partition_lengths[pos + blockDim.x + threadIdx.x] : 0;
__syncthreads(); __syncthreads();
for (int porder = max_porder - 1; porder >= 0; porder--)
int in_pos = (threadIdx.x << 1);
int out_pos = (1 << max_porder) + threadIdx.x;
int bs;
for (bs = 1 << (max_porder - 1); bs > 32; bs >>= 1)
{ {
const int in_pos = (2 << max_porder) - (4 << porder); if (threadIdx.x < bs) shared.data[out_pos] = shared.data[in_pos] + shared.data[in_pos + 1];
const int out_pos = (2 << max_porder) - (2 << porder); in_pos += bs << 1;
if (threadIdx.x < (1 << porder)) shared.data[out_pos + threadIdx.x] = shared.data[in_pos + (threadIdx.x << 1)] + shared.data[in_pos + (threadIdx.x << 1) + 1]; out_pos += bs;
__syncthreads(); __syncthreads();
} }
if (threadIdx.x < 32)
for (; bs > 0; bs >>= 1)
{
shared.data[out_pos] = shared.data[in_pos] + shared.data[in_pos + 1];
in_pos += bs << 1;
out_pos += bs;
}
__syncthreads();
if (threadIdx.x < (1 << max_porder)) if (threadIdx.x < (1 << max_porder))
partition_lengths[pos + (1 << max_porder) + threadIdx.x] = shared.data[(1 << max_porder) + threadIdx.x]; partition_lengths[pos + (1 << max_porder) + threadIdx.x] = shared.data[(1 << max_porder) + threadIdx.x];
if (blockDim.x + threadIdx.x < (1 << max_porder))
partition_lengths[pos + (1 << max_porder) + blockDim.x + threadIdx.x] = shared.data[(1 << max_porder) + blockDim.x + threadIdx.x];
} }
// Finds optimal rice parameter for up to 16 partitions at a time. // Finds optimal rice parameter for up to 16 partitions at a time.
@@ -1014,45 +1052,36 @@ extern "C" __global__ void cudaFindRiceParameter(
__shared__ struct { __shared__ struct {
volatile int length[256]; volatile int length[256];
volatile int index[256]; volatile int index[256];
volatile int outlen[32];
volatile int outidx[32];
} shared; } shared;
const int tid = threadIdx.x + (threadIdx.y << 3); const int tid = threadIdx.x + (threadIdx.y << 5);
const int parts = min(32, 2 << max_porder); const int parts = min(32, 2 << max_porder);
const int pos = (15 << (max_porder + 1)) * blockIdx.y + ((tid >> 5) << (max_porder + 1)); const int pos = (15 << (max_porder + 1)) * blockIdx.y + (threadIdx.y << (max_porder + 1));
// read length for 32 partitions // read length for 32 partitions
shared.index[tid] = ((tid & 31) < parts) ? partition_lengths[pos + blockIdx.x * 32 + (tid & 31)] : 0xffffff; int l1 = (threadIdx.x < parts) ? partition_lengths[pos + blockIdx.x * 32 + threadIdx.x] : 0xffffff;
shared.length[tid] = ((tid >> 5) + 8 <= 14 && (tid & 31) < parts) ? partition_lengths[pos + (8 << (max_porder + 1)) + blockIdx.x * 32 + (tid & 31)] : 0xffffff; int l2 = (threadIdx.y + 8 <= 14 && threadIdx.x < parts) ? partition_lengths[pos + (8 << (max_porder + 1)) + blockIdx.x * 32 + threadIdx.x] : 0xffffff;
__syncthreads();
// transpose
int l1 = shared.index[threadIdx.y + (threadIdx.x << 5)];
int l2 = shared.length[threadIdx.y + (threadIdx.x << 5)];
__syncthreads();
// find best rice parameter // find best rice parameter
shared.index[tid] = threadIdx.x + ((l2 < l1) << 3); shared.index[tid] = threadIdx.y + ((l2 < l1) << 3);
shared.length[tid] = l1 = min(l1, l2); shared.length[tid] = l1 = min(l1, l2);
#pragma unroll 2 __syncthreads();
for (int sh = 2; sh > 0; sh --) #pragma unroll 3
if (threadIdx.x < (1 << sh)) for (int sh = 7; sh >= 5; sh --)
{
if (tid < (1 << sh))
{ {
l2 = shared.length[tid + (1 << sh)]; l2 = shared.length[tid + (1 << sh)];
shared.index[tid] = shared.index[tid + ((l2 < l1) << sh)]; shared.index[tid] = shared.index[tid + ((l2 < l1) << sh)];
shared.length[tid] = l1 = min(l1, l2); shared.length[tid] = l1 = min(l1, l2);
} }
if (threadIdx.x == 0 && threadIdx.y < parts)
{
l2 = shared.length[tid + 1];
shared.outidx[threadIdx.y] = shared.index[tid + (l2 < l1)];
shared.outlen[threadIdx.y] = min(l1, l2);
}
__syncthreads(); __syncthreads();
}
if (tid < parts)
{
// output rice parameter // output rice parameter
if (tid < parts) rice_parameters[(blockIdx.y << (max_porder + 2)) + blockIdx.x * parts + tid] = shared.index[tid];
rice_parameters[(blockIdx.y << (max_porder + 2)) + blockIdx.x * parts + tid] = shared.outidx[tid];
// output length // output length
if (tid < parts) rice_parameters[(blockIdx.y << (max_porder + 2)) + (1 << (max_porder + 1)) + blockIdx.x * parts + tid] = shared.length[tid];
rice_parameters[(blockIdx.y << (max_porder + 2)) + (1 << (max_porder + 1)) + blockIdx.x * parts + tid] = shared.outlen[tid]; }
} }
extern "C" __global__ void cudaFindPartitionOrder( extern "C" __global__ void cudaFindPartitionOrder(

532
CUETools.FlaCuda/flacuda.cubin
View File

@@ -383,16 +383,16 @@ code {
code { code {
name = cudaSumPartition name = cudaSumPartition
lmem = 0 lmem = 0
smem = 2072 smem = 2200
reg = 6 reg = 8
bar = 1 bar = 1
const { const {
segname = const segname = const
segnum = 1 segnum = 1
offset = 0 offset = 0
bytes = 4 bytes = 8
mem { mem {
0xffffffff 0x00000020 0x0000001f
} }
} }
bincode { bincode {
@@ -404,29 +404,56 @@ code {
0xa0004c0d 0x04200780 0x30100811 0xc4100780 0xa0004c0d 0x04200780 0x30100811 0xc4100780
0x30010605 0xc4000780 0x60004e01 0x00210780 0x30010605 0xc4000780 0x60004e01 0x00210780
0x20018000 0x20008400 0x30020001 0xc4100780 0x20018000 0x20008400 0x30020001 0xc4100780
0x2000c801 0x04200780 0xd00e000d 0x80c00780
0x10014003 0x00000780 0x1000f80d 0x0403c780
0xa0004205 0x04200782 0x1100ea00 0x20018410
0x00020405 0xc0000780 0x30000801 0xe40007e0
0x04000c01 0xe420c780 0x00020805 0xc0000780
0x307c01fd 0x640087d8 0xa002c003 0x00000000
0x1002b003 0x00002680 0x2101ea0d 0x00000003
0x100f8001 0x00000003 0x30030001 0xc4000780
0x40014e15 0x00200780 0xa0004c11 0x04200780
0x30100a15 0xc4100780 0x3003080d 0xc4000780
0x60004e01 0x00214780 0x2003800c 0x20028200
0x20000001 0x0400c780 0x30020001 0xc4100780
0x2000c801 0x04200780 0xd00e0001 0x80c00780 0x2000c801 0x04200780 0xd00e0001 0x80c00780
0x10014003 0x00000780 0x1000f801 0x0403c780 0x1002c003 0x00000780 0x1000f801 0x0403c780
0x00020405 0xc0000782 0x04000c01 0xe4200780 0x04000c01 0xe4200782 0x861ffe03 0x00000000
0x861ffe03 0x00000000 0x2100ca05 0x046007d0 0x10018011 0x00000003 0x1000ca01 0x0423c780
0x1002f003 0x00001980 0x300105fd 0xe40007d8 0x213fea15 0x0fffffff 0x3000080d 0xc4000780
0xa002a003 0x00000000 0x1002a003 0x00001280 0x30050815 0xc4000780 0x20000411 0x0400c780
0x1002800d 0x00000003 0x1000ca01 0x0423c780 0x30800bfd 0x6c40c7e8 0x30010419 0xc4100780
0x10048011 0x00000003 0x30000601 0xc4000780 0x00020805 0xc0000780 0x10044003 0x00002280
0x30010811 0xc4000780 0x30010415 0xc4100780 0x30020bfd 0x6400c7e8 0x00020c09 0xc0002500
0x20400011 0x04010780 0x3001060d 0xc4000780 0x1800ce01 0x0423e500 0x0002080d 0xc0002500
0x20058810 0x20438000 0x00020805 0xc0000780 0x2800cc01 0x04202500 0x0c000c01 0xe4202500
0x2000840c 0x1500ee00 0x00020609 0xc0000780 0x30010a01 0xc4100780 0x20008c18 0x20048a10
0x2400cc01 0x04200780 0x08000c01 0xe4200780 0x861ffe03 0x00000000 0x30010a15 0xec100780
0xf0000001 0xe0000002 0x861ffe03 0x00000000 0x30800bfd 0x6c4107e8 0x10038003 0x00002280
0x203f8205 0x0fffffff 0x308003fd 0x6c4147d8 0x308105fd 0x644107e8 0xa0055003 0x00000000
0x10019003 0x00001280 0x30000003 0x00000100 0x10055003 0x00002280 0x307c0bfd 0x6c00c7e8
0x2101ea0d 0x00000003 0x100f8001 0x00000003 0x10055003 0x00002280 0xa0054003 0x00000000
0x30030005 0xc4000780 0x40034e11 0x00200780 0x0002080d 0xc0000780 0x00020c09 0xc0000780
0xa0004c01 0x04200780 0x30100815 0xc4100780 0x30010a1d 0xc4100780 0x20058810 0x1900ee00
0x30030011 0xc4000780 0x1001800d 0x00000003 0x30010a15 0xec100780 0x2800cc01 0x04200780
0x1000ca01 0x0423c780 0x60024e05 0x00214780 0x307c0bfd 0x6c0107e8 0x0c000c01 0xe4200780
0x30000601 0xc4000780 0x20048204 0x20018004 0x20000c19 0x0401c780 0x1004a003 0x00002280
0x2000840c 0x20018400 0x00020605 0xc0000780 0xf0000001 0xe0000002 0xf0000001 0xe0000002
0x861ffe03 0x00000000 0xa0066003 0x00000000
0x10066003 0x00000100 0x2101ea11 0x00000003
0x100f8001 0x00000003 0x30040001 0xc4000780
0x40014e19 0x00200780 0xa0004c15 0x04200780
0x30100c19 0xc4100780 0x30040a11 0xc4000780
0x60004e01 0x00218780 0x20048000 0x20008600
0x20000401 0x04000780 0x30020011 0xc4100780
0x1500ec00 0x2104e810 0xd00e0801 0xa0c00780
0xf0000001 0xe0000002 0x30000003 0x00001100
0x2101ea11 0x00000003 0x100f8001 0x00000003
0x30040001 0xc4000780 0x40014e19 0x00200780
0xa0004c15 0x04200780 0x30100c19 0xc4100780
0x30040a11 0xc4000780 0x60004e01 0x00218780
0x20048000 0x20008600 0x2003820c 0x20008200
0x20038404 0x20008400 0x00020205 0xc0000780
0x30020005 0xc4100780 0x1500ec00 0x2101e804 0x30020005 0xc4100780 0x1500ec00 0x2101e804
0xd00e0201 0xa0c00781 0xd00e0201 0xa0c00781
} }
@@ -709,10 +736,10 @@ code {
} }
} }
code { code {
name = cudaCalcPartition1 name = cudaCalcPartition16
lmem = 0 lmem = 0
smem = 3308 smem = 1388
reg = 11 reg = 9
bar = 1 bar = 1
const { const {
segname = const segname = const
@@ -720,79 +747,89 @@ code {
offset = 0 offset = 0
bytes = 24 bytes = 24
mem { mem {
0x000003ff 0x0000002f 0x000fffff 0x00000001 0x000003ff 0x0000002f 0x0000001f 0x000fffff
0x0000000f 0x0000000e 0x0000000e 0x0000000f
} }
} }
bincode { bincode {
0x10000005 0x0403c780 0xd0800601 0x00400780 0x10000005 0x0403c780 0xd0800601 0x00400780
0xa0000001 0x04000780 0xa0000415 0x04000780 0xa0000001 0x04000780 0xa0000409 0x04000780
0x30040005 0xc4100780 0x20000a21 0x04004780 0x30040005 0xc4100780 0x2000040d 0x04004780
0x308111fd 0x644107c8 0xa0012003 0x00000000 0x308107fd 0x644107c8 0xa0012003 0x00000000
0x30021019 0xc4100780 0x10012003 0x00000280 0x30020619 0xc4100780 0x10012003 0x00000280
0xa0004e05 0x04200780 0x30070209 0xc4100780 0xa0004e05 0x04200780 0x30070211 0xc4100780
0x30060205 0xc4100780 0x20018404 0x2101ee04 0x30060205 0xc4100780 0x20018804 0x2101ee04
0x20000c05 0x04004780 0xd00e0205 0x80c00780 0x20000c05 0x04004780 0xd00e0205 0x80c00780
0x00000c05 0xc0000780 0x04061601 0xe4204780 0x00000c05 0xc0000780 0x04025601 0xe4204780
0xf0000001 0xe0000002 0x861ffe03 0x00000000 0xf0000001 0xe0000002 0x861ffe03 0x00000000
0xa0004c0d 0x04200780 0x1100f404 0x1100f208 0xa0004c11 0x04200780 0x30080805 0xc4100780
0x4006061c 0x40050c24 0x10018029 0x00000003 0x308207fd 0x6c4107c8 0xa002c003 0x00000000
0x1000d011 0x0423c780 0x30100e1d 0xc4100780 0x20000215 0x0400c780 0x1002c003 0x00000280
0x30101225 0xc4100780 0x30041411 0xc4000780 0xd0095805 0x20000780 0x2400c005 0x0420c780
0x6006041d 0x0001c780 0x60040c0d 0x00024780 0x30010a05 0xac000780 0x308203fd 0x6c40c7c8
0x30048e10 0x1100f208 0x40060625 0x00000780 0xa002a003 0x00000000 0x10029003 0x00000280
0x3004d411 0xa4200780 0x60070425 0x00024780 0xd0096005 0x20000780 0x2400c005 0x04214780
0x40051029 0x00000780 0x30101225 0xc4100780 0x30020205 0xc4100780 0x2000cc05 0x04204780
0x60041229 0x00028780 0x60060405 0x00024780 0x20008205 0x0ffffffb 0xd00e021d 0x80c00780
0xd0185805 0x20000780 0x3010140d 0xc4100780 0x1400d405 0x0423c780 0x30010e05 0xec000780
0x20000205 0x04020780 0x60041009 0x0000c780 0x1002a003 0x00000780 0x1000f805 0x0403c780
0x3401c1fd 0x6c20c7c8 0x300211fd 0x6c0042c8 0x00000c05 0xc0000782 0x04001601 0xe4204780
0xa0035003 0x00000000 0x10034003 0x00000100 0xd0096005 0x20000782 0x2400c005 0x04214780
0xd018a805 0x20000780 0x2400c005 0x04204780 0x30020205 0xc4100780 0x2000cc05 0x04204780
0x30020205 0xc4100780 0x2000ca05 0x04204780 0xd00e021d 0x80c00780 0x1400d405 0x0423c780
0xd00e0205 0x80c00780 0x10035003 0x00000780 0x30010e05 0xec000780 0x00000c05 0xc0000780
0x1000f805 0x0403c780 0x301f0209 0xec100782 0x04005601 0xe4204780 0x861ffe03 0x00000000
0x30010205 0xc4100780 0xd0010405 0x04008780 0xd0095805 0x20000780 0x3500e01d 0x00000003
0x00000c05 0xc0000780 0x30820205 0xac400780 0x307c0ffd 0x6c0187c8 0x1000f821 0x0403c780
0x04001601 0xe4204780 0x861ffe03 0x00000000 0x1400c005 0x0423c780 0x1004d003 0x00000280
0x307c0a05 0x64008780 0x30000809 0x64010780 0x20000e05 0x0400c780 0x200b821d 0x00000003
0xd0830205 0x04400780 0xd0830409 0x04400780 0x102c8005 0x00000003 0x00020e09 0xc0000780
0xd002020d 0x04000780 0x307cd3fd 0x6c2107c8 0x00000205 0xc0000780 0x1000f81d 0x0403c780
0x1000f821 0x0403c780 0x00000c05 0xc0000780 0xd4098011 0x20000780 0xd801000d 0x20000780
0x1000f809 0x0403c780 0x04021601 0xe43f0780 0x20018e1d 0x00000003 0x1000c005 0x0423c784
0x1005b003 0x00000100 0x1000d205 0x0423c780 0xd0095811 0x20000780 0x6c01c021 0x80220780
0x40010425 0x00000780 0x60000625 0x00024780 0x3007c1fd 0x6c2147cc 0xd8000809 0x20000780
0x30101225 0xc4100780 0x60000429 0x00024780 0xd4000805 0x20000780 0x1000c005 0x0423c784
0x20001405 0x04014780 0x200b8225 0x00000003 0x10042003 0x00000280 0xd0096805 0x20000780
0x2000d229 0x04228780 0x00021205 0xc0000780 0x1400c01d 0x0423c780 0x00000c05 0xc0000780
0xa005a003 0x00000000 0x20000a25 0x04028780 0x30071019 0xec000780 0xd4015805 0x20000780
0x3408c029 0xec200780 0x20108205 0x00000003 0x300503fd 0x6c0107c8 0xa005d003 0x00000000
0x20000409 0x04028780 0x00000c09 0xc0000780 0x2440c005 0x04218780 0x1005c003 0x00000280
0x300903fd 0x6c0047d8 0xd4008005 0x20000780 0xd009a805 0x20000780 0x2400c015 0x04214780
0x08021601 0xe4208780 0x10052003 0x00001280 0x30020a15 0xc4100780 0x2000ca15 0x04214780
0xf0000001 0xe0000002 0x00000c05 0xc0000780 0xd00e0a05 0xa0c00780 0x1005d003 0x00000780
0xd4086009 0x20000780 0x2800ce05 0x04208780 0x1000f805 0x0403c780 0xf0000001 0xe0000002
0x04021601 0xe4204780 0x2800c605 0x04204780 0x861ffe03 0x00000000 0x301f0215 0xec100780
0x04021601 0xe4204780 0x2800c205 0x04204780 0x30010219 0xc4100780 0x20000605 0x04000780
0x04021601 0xe4204780 0x2800c005 0x04204780 0xd0060a0d 0x04008780 0x00020205 0xc0000780
0x307c07fd 0x6c0087d8 0x04021601 0xe4204780 0x30830605 0xac400780 0x04001601 0xe4204780
0x30041009 0xc4101500 0x20000009 0x04009500 0x861ffe03 0x00000000 0x40518405 0x00000003
0x00020405 0xc0001500 0x04041601 0xe4205500 0x00020205 0xc0000780 0x3400d605 0xec200780
0x20019021 0x00000003 0x308411fd 0x6c4147d8 0x3400d80d 0xec200780 0x3400da15 0xec200780
0x10043003 0x00001280 0x861ffe03 0x00000000 0x3400dc19 0xec200780 0x20038204 0x20068a0c
0x300509fd 0x640107c8 0x308501fd 0x6440c2c8 0x3400de15 0xec200780 0x3400e019 0xec200780
0x30000003 0x00000100 0xd0185805 0x20000780 0x20038204 0x20068a0c 0x3400e215 0xec200780
0x1000d20d 0x0423c780 0x20018009 0x00000003 0x3400e419 0xec200780 0x20038204 0x20068a0c
0x1000d205 0x0423c780 0x3503e00c 0x40030810 0x3400e615 0xec200780 0x3400e819 0xec200780
0x610f2e01 0x00000003 0x60020a29 0x00010780 0x20038204 0x20068a0c 0x3400ea15 0xec200780
0x40070825 0x00000780 0x2101f021 0x00000003 0x3400ec19 0xec200780 0x20038204 0x20068a0c
0x20000e11 0x040147c0 0x30101429 0xc4100780 0x3400ee15 0xec200780 0x3400f019 0xec200780
0x60060a1d 0x00024780 0x30080015 0xc4000780 0x20038204 0x20068a0c 0x3400f215 0xec200780
0x60020801 0x00028780 0x30100e1d 0xc4100780 0x3400f419 0xec200780 0x20038204 0x20068a0c
0x20000805 0x04014780 0x00000c05 0xc0000780 0x308401fd 0x644107c8 0x20000215 0x0400c780
0x60060801 0x0001c100 0x30020205 0xc4100780 0x30000003 0x00000280 0x200009fd 0x040087c8
0xd4105805 0x20000780 0x2101e804 0x2500e000 0xa0092003 0x00000000 0x1008f003 0x00000280
0xd00e0201 0xa0c00781 0xd0095805 0x20000780 0x20018005 0x00000003
0x3510e00d 0x00000003 0x40070419 0x00000780
0x60060619 0x00018780 0x30100c19 0xc4100780
0x60060405 0x00018780 0x20000205 0x04014780
0x10092003 0x00000780 0x30040005 0xc4100780
0x20000a05 0x04004780 0x20108205 0x00000003
0xf0000001 0xe0000002 0x610f2e01 0x00000003
0x2101f00d 0x00000003 0x3003000d 0xc4000780
0x30040801 0xc4100780 0x20038000 0x20008400
0x30020001 0xc4100780 0x2000c801 0x04200780
0xd00e0005 0xa0c00781
} }
} }
code { code {
@@ -1059,93 +1096,89 @@ code {
code { code {
name = cudaFindRiceParameter name = cudaFindRiceParameter
lmem = 0 lmem = 0
smem = 2332 smem = 2076
reg = 10 reg = 9
bar = 1 bar = 1
const { const {
segname = const segname = const
segnum = 1 segnum = 1
offset = 0 offset = 0
bytes = 20 bytes = 24
mem { mem {
0x000003ff 0x00000020 0x0000001f 0x00000001 0x00000020 0x000003ff 0x00000001 0x0000000e
0x0000000e 0x00000080 0x00000040
} }
} }
bincode { bincode {
0xd0800205 0x00400780 0xa0000211 0x04000780
0x10028009 0x00000003 0x1000cc05 0x0423c780 0x10028009 0x00000003 0x1000cc05 0x0423c780
0xa0000015 0x04000780 0x30030801 0xc4100780 0x30010409 0xc4000780 0x10000005 0x0403c780
0x30010405 0xc4000780 0x20000a01 0x04000780 0x30800409 0xac400780 0xa0000401 0x04000780
0x30810209 0xac400780 0xd0820005 0x04400780 0x3000040d 0x640107d0 0xd0820609 0x00400780
0x3001040d 0x6c0107d0 0xa00007fd 0x0c0147c8 0xa00007fd 0x0c0147c8 0xa001b003 0x00000000
0xa001e003 0x00000000 0x30050019 0xec100780 0xa0000405 0x04000780 0x1001a003 0x00001100
0x1001d003 0x00001100 0x2101ec1d 0x00000003 0x2101ec11 0x00000003 0x100f800d 0x00000003
0x100f800d 0x00000003 0x3007060d 0xc4000780 0x3004060d 0xc4000780 0x40074e15 0x00200780
0x40074e21 0x00200780 0x30101021 0xc4100780 0x30100a15 0xc4100780 0x60064e15 0x00214780
0x60064e21 0x00220780 0x30070c0d 0xc4000780 0x3004020d 0xc4000780 0x20000a11 0x0400c780
0x2000101d 0x0400c780 0x60824c0d 0x00604780 0x60804c0d 0x00600780 0x2000060d 0x04010780
0x2000060d 0x0401c780 0x3002060d 0xc4100780 0x3002060d 0xc4100780 0x2000ca0d 0x0420c780
0x2000ca0d 0x0420c780 0xd00e060d 0x80c00780 0xd00e0611 0x80c00780 0x1001b003 0x00000780
0x1001e003 0x00000780 0x103f800d 0x000fffff 0x103f8011 0x000fffff 0x1000f80d 0x0403c782
0x1000f81d 0x0403c782 0x20088c21 0x00000003 0x20088215 0x00000003 0x30830a19 0x6440c780
0x30841021 0x6c40c780 0x1000061d 0x2440c280 0x1000040d 0x2440c280 0xa0000c19 0x2c014780
0xa0001021 0x2c014780 0x00020005 0xc0000780 0xd00607fd 0x040007c8 0xa0035003 0x00000000
0xd0080ffd 0x040007c8 0x04020e01 0xe420c780 0x10034003 0x00000100 0x2101ec19 0x00000003
0xa0039003 0x00000000 0x10038003 0x00000100 0x100f800d 0x00000003 0x3006060d 0xc4000780
0x2101ec1d 0x00000003 0x100f800d 0x00000003 0x40074e1d 0x00200780 0x30100e1d 0xc4100780
0x3007060d 0xc4000780 0x40074e21 0x00200780 0x60064e21 0x0021c780 0x3006021d 0xc4000780
0x30101021 0xc4100780 0x60064e0d 0x00220780 0x1008800d 0x00000003 0x2000101d 0x0401c780
0x10088021 0x00000003 0x30070c25 0xc4000780 0x3006060d 0xc4000780 0x2000060d 0x0401c780
0x30071019 0xc4000780 0x2009860c 0x20038c0c 0x61202c0d 0x00000003 0x2000000d 0x0400c780
0x61202c0d 0x00000003 0x20000205 0x0400c780 0x3002060d 0xc4100780 0x2000ca0d 0x0420c780
0x30020205 0xc4100780 0x2000ca05 0x04204780 0xd00e0619 0x80c00780 0x10035003 0x00000780
0xd00e0205 0x80c00780 0x10039003 0x00000780 0x103f8019 0x000fffff 0x3005020d 0xc4100782
0x103f8005 0x000fffff 0x04000e01 0xe4204782 0x20000601 0x04000780 0x300609fd 0x6c0107c8
0x861ffe03 0x00000000 0x30050a05 0xc4100780 0x1000020d 0x0403c780 0x10000a0d 0x0403c280
0x20000205 0x04010780 0x00020209 0xc0000780 0x00020005 0xc0000780 0x30060805 0xac000780
0xd808380d 0x20000780 0x1900ee18 0x1d00e004 0x04020e01 0xe420c780 0x04000e01 0xe4204780
0x861ffe03 0x00000000 0x300603fd 0x6c0107c8 0x861ffe03 0x00000000 0x308401fd 0x6c4187c8
0x20088a0d 0x00000003 0x10000a0d 0x0403c500 0xa004e003 0x00000000 0x1004e003 0x00000280
0x30060219 0xac000780 0x04020e01 0xe420c780 0x20008011 0x0000000b 0x00020809 0xc0000780
0x30020bfd 0xe41007c8 0xa0055003 0x00000000 0x1000000d 0x0403c780 0x3801cffd 0x6c2047c8
0x04000e01 0xe4218780 0x10055003 0x00000280 0x1000080d 0x0403c280 0x00020605 0xc0000780
0x2004800d 0x00000003 0x00020609 0xc0000780 0xd408380d 0x20000780 0x00020005 0xc0000780
0x10000005 0x0403c780 0x3806cffd 0x6c2047c8 0x3801ce05 0xac200780 0x1c00c00d 0x0423c780
0x10000605 0x0403c280 0x0002020d 0xc0000780 0x04020e01 0xe420c780 0x04000e01 0xe4204780
0xdc08380d 0x20000780 0x3806ce19 0xac200780
0x1c00c005 0x0423c780 0x04020e01 0xe4204780
0x04000e01 0xe4218780 0x30010bfd 0xe41007ca
0xa0063003 0x00000000 0x10063003 0x00000280
0x2002800d 0x00000003 0x00020609 0xc0000780
0x10000005 0x0403c780 0x3806cffd 0x6c2047c8
0x10000605 0x0403c280 0x0002020d 0xc0000780
0xdc08380d 0x20000780 0x3806ce19 0xac200780
0x1c00c005 0x0423c780 0x04020e01 0xe4204780
0x04000e01 0xe4218780 0x307c0bfd 0x640087ca
0x300405fd 0x640102c8 0xa0070003 0x00000000
0x10070003 0x00000100 0x3406d005 0x6c204780
0x30000205 0x04000780 0x00020209 0xc0000780
0xd808380d 0x20000780 0x00020809 0xc0000780
0x3406d00d 0xac200780 0x1c00c005 0x0423c780
0x08044e01 0xe4204780 0x08040e01 0xe420c780
0xf0000001 0xe0000002 0x861ffe03 0x00000000 0xf0000001 0xe0000002 0x861ffe03 0x00000000
0x30000405 0x6c0107d0 0xa00003fd 0x0c0147c8 0x308501fd 0x6c4187c8 0xa005f003 0x00000000
0xa0081003 0x00000000 0x10081003 0x00001100 0x1005f003 0x00000280 0x20008011 0x00000007
0x40054c11 0x00200780 0xa0004e05 0x04200780 0x00020809 0xc0000780 0x1000000d 0x0403c780
0x2102ec0d 0x00000003 0x30100811 0xc4100780 0x3801cffd 0x6c2047c8 0x1000080d 0x0403c280
0x30030205 0xc4000780 0x60044c0d 0x00210780 0x00020605 0xc0000780 0xd408380d 0x20000780
0x20018604 0x20018004 0xd4113809 0x20000780 0x00020005 0xc0000780 0x3801ce05 0xac200780
0x3002020d 0xc4100780 0x1900e004 0x2103e80c 0x1c00c00d 0x0423c780 0x04020e01 0xe420c780
0xd00e0605 0xa0c00780 0xf0000001 0xe0000002 0x04000e01 0xe4204780 0xf0000001 0xe0000002
0x30000003 0x00000100 0x2101ec0d 0x00000003 0x861ffe03 0x00000000 0x308001fd 0x6c4187c8
0x40054c19 0x00200780 0x10018005 0x00000003 0xa0070003 0x00000000 0x10070003 0x00000280
0xa0004e11 0x04200780 0x2102ec15 0x00000003 0x20208011 0x00000003 0x00020809 0xc0000780
0x30100c19 0xc4100780 0x30030205 0xc4000780 0x1000000d 0x0403c780 0x3801cffd 0x6c2047c8
0x3005080d 0xc4000780 0x60044c09 0x00218780 0x1000080d 0x0403c280 0x00020605 0xc0000780
0x20038204 0x20008400 0x20000001 0x04004780 0xd408380d 0x20000780 0x00020005 0xc0000780
0xd4103805 0x20000780 0x30020005 0xc4100780 0x3801ce0d 0xac200780 0x1c00c005 0x0423c780
0x1500e000 0x2101e804 0xd00e0201 0xa0c00781 0x04020e01 0xe4204780 0x04000e01 0xe420c780
0xf0000001 0xe0000002 0x861ffe03 0x00000000
0x300005fd 0x6c00c7c8 0x30000003 0x00000280
0xa0004c0d 0x04200780 0x40060a05 0x00000780
0x2102ec19 0x00000003 0xa0004e15 0x04200780
0x30100211 0xc4100780 0x30060a05 0xc4000780
0x60060809 0x00010780 0x2101ec15 0x00000003
0x10018011 0x00000003 0x2000040d 0x04004780
0x30050811 0xc4000780 0x00020005 0xc0000780
0x2003800c 0x20028000 0x20000209 0x04010780
0xd4083809 0x20000780 0x30020605 0xc4100780
0x20028008 0x1900e000 0x2000c805 0x04204780
0x30020409 0xc4100780 0xd00e0201 0xa0c00780
0x1500ee00 0x2102e804 0xd00e0201 0xa0c00781
} }
} }
code { code {
@@ -1418,7 +1451,7 @@ code {
name = cudaCalcPartition name = cudaCalcPartition
lmem = 0 lmem = 0
smem = 1388 smem = 1388
reg = 11 reg = 14
bar = 1 bar = 1
const { const {
segname = const segname = const
@@ -1431,97 +1464,110 @@ code {
} }
} }
bincode { bincode {
0xd0800205 0x00400780 0xa0000209 0x04000780 0xd0800205 0x00400780 0xa0000215 0x04000780
0xa0000019 0x04000780 0x30040401 0xc4100780 0xa0000019 0x04000780 0x30040a01 0xc4100780
0x20000c11 0x04000780 0x308109fd 0x644107c8 0x20000c05 0x04000780 0x308103fd 0x644107c8
0xa0011003 0x00000000 0x3002081d 0xc4100780 0xa0011003 0x00000000 0x3002021d 0xc4100780
0x10011003 0x00000280 0xa0004e01 0x04200780 0x10011003 0x00000280 0xa0004e01 0x04200780
0x30070005 0xc4100780 0x30060001 0xc4100780 0x30070009 0xc4100780 0x30060001 0xc4100780
0x20008200 0x2100ee00 0x20000e01 0x04000780 0x20008400 0x2100ee00 0x20000e01 0x04000780
0xd00e0001 0x80c00780 0x00000e05 0xc0000780 0xd00e0001 0x80c00780 0x00000e05 0xc0000780
0x04025601 0xe4200780 0xf0000001 0xe0000002 0x04025601 0xe4200780 0xf0000001 0xe0000002
0x861ffe03 0x00000000 0xa0004c05 0x04200780 0x861ffe03 0x00000000 0xa0004c09 0x04200780
0x1000d201 0x0423c780 0x4002020d 0x00000780 0x1000d201 0x0423c780 0x4004020d 0x00000780
0x3010060d 0xc4100780 0x6002000d 0x0000c780 0x3010060d 0xc4100780 0x6004000d 0x0000c780
0x1000d401 0x0423c780 0x40010c15 0x00000780 0x1000d401 0x0423c780 0x40010c11 0x00000780
0x60000e15 0x00014780 0x30100a15 0xc4100780 0x60000e11 0x00010780 0x30100811 0xc4100780
0x60000c01 0x00014780 0x308209fd 0x6c4107c8 0x60000c01 0x00010780 0x308203fd 0x6c4107c8
0xa0033003 0x00000000 0x20000015 0x04010780 0xa0033003 0x00000000 0x20000025 0x04004780
0x10033003 0x00000280 0xd0095805 0x20000780 0x10033003 0x00000280 0xd0095805 0x20000780
0x2400c001 0x04210780 0x30000a01 0xac000780 0x2400c001 0x04204780 0x30001201 0xac000780
0x308201fd 0x6c40c7c8 0xa0031003 0x00000000 0x308201fd 0x6c40c7c8 0xa0031003 0x00000000
0x10030003 0x00000280 0xd0096005 0x20000780 0x10030003 0x00000280 0xd0096005 0x20000780
0x2400c001 0x04214780 0x30020001 0xc4100780 0x2400c001 0x04224780 0x30020001 0xc4100780
0x2000cc01 0x04200780 0x20008001 0x0ffffffb 0x2000cc01 0x04200780 0x20008001 0x0ffffffb
0xd00e000d 0x80c00780 0x1400d401 0x0423c780 0xd00e000d 0x80c00780 0x1400d401 0x0423c780
0x30000601 0xec000780 0x10031003 0x00000780 0x30000601 0xec000780 0x10031003 0x00000780
0x1000f801 0x0403c780 0x00000e05 0xc0000782 0x1000f801 0x0403c780 0x00000e05 0xc0000782
0x04001601 0xe4200780 0x1000d401 0x0423c782 0x04001601 0xe4200780 0x1000d401 0x0423c782
0x4003000d 0x00000780 0x60020225 0x0000c780 0x4005000d 0x00000780 0x60040211 0x0000c780
0x10018021 0x00000003 0x1000d00d 0x0423c780 0x10018021 0x00000003 0x1000d00d 0x0423c780
0x30101225 0xc4100780 0x3003100d 0xc4000780 0x30100811 0xc4100780 0x3003100d 0xc4000780
0x60020021 0x00024780 0x3003900c 0x1100f200 0x60040021 0x00010780 0x3003900c 0x1100f200
0x3003d40d 0xa4200780 0x40010c25 0x00000780 0x3003d411 0xa4200780 0x4001100d 0x00000780
0x60000e25 0x00024780 0x30101225 0xc4100780 0x6000120d 0x0000c780 0x3010060d 0xc4100780
0x60000c01 0x00024780 0x30040001 0x6c0107d0 0x60001001 0x0000c780 0x30010001 0x6c0107d0
0xa00001fd 0x0c0147c8 0xa004e003 0x00000000 0xa00001fd 0x0c0147c8 0xa004e003 0x00000000
0x1004d003 0x00001100 0xd0096005 0x20000780 0x1004d003 0x00001100 0xd0096005 0x20000780
0x2400c001 0x04214780 0x30020001 0xc4100780 0x2400c001 0x04224780 0x30020001 0xc4100780
0x2000cc01 0x04200780 0xd00e0025 0x80c00780 0x2000cc01 0x04200780 0xd00e000d 0x80c00780
0x1400d401 0x0423c780 0x30001201 0xec000780 0x1400d401 0x0423c780 0x30000601 0xec000780
0x1004e003 0x00000780 0x1000f801 0x0403c780 0x1004e003 0x00000780 0x1000f801 0x0403c780
0x00000e05 0xc0000782 0x04005601 0xe4200780 0x00000e05 0xc0000782 0x04005601 0xe4200780
0x861ffe03 0x00000000 0xd0095805 0x20000780 0x861ffe03 0x00000000 0xd0095805 0x20000780
0x3500e029 0x00000003 0x307c15fd 0x6c0187d8 0x3500e029 0x00000003 0x307c15fd 0x6c0187d8
0x1000f825 0x0403c780 0x1400c001 0x0423c780 0x1000f80d 0x0403c780 0x1400c001 0x0423c780
0x10068003 0x00001280 0x20001401 0x04010780 0x10068003 0x00001280 0x20001401 0x04004780
0x200b8001 0x00000003 0x102c8011 0x00000003 0x200b8029 0x00000003 0x102c8001 0x00000003
0x00020009 0xc0000780 0x00000805 0xc0000780 0x00021409 0xc0000780 0x00000005 0xc0000780
0x1000f811 0x0403c780 0xd4098011 0x20000780 0x1000f829 0x0403c780 0xd4098011 0x20000780
0xd801000d 0x20000780 0x20018811 0x00000003 0xd801000d 0x20000780 0x20019429 0x00000003
0x1000c001 0x0423c784 0xd0095811 0x20000780 0x1000c001 0x0423c784 0xd0095811 0x20000780
0x6c00c025 0x80224780 0x3004c1fd 0x6c2147dc 0x6c00c00d 0x8020c780 0x300ac1fd 0x6c2147dc
0xd8000809 0x20000780 0xd4000805 0x20000780 0xd8000809 0x20000780 0xd4000805 0x20000780
0x1000c001 0x0423c784 0x1005d003 0x00001280 0x1000c001 0x0423c784 0x1005d003 0x00001280
0xd0096809 0x20000780 0x30050029 0x6c00c780 0xd0096809 0x20000780 0x3009002d 0x6c00c780
0x1000f811 0x0403c780 0x00000e05 0xc0000780 0x1000f829 0x0403c780 0x00000e05 0xc0000780
0x1800c001 0x0423c780 0xa0001429 0x2c014780 0x1800c001 0x0423c780 0xa000161d 0x2c014780
0x10000611 0x2440c280 0x30001201 0xec000780 0x10000629 0x2440c280 0x30000601 0xec000780
0xd4015805 0x20000780 0xd00a09fd 0x040007c8 0xd4015805 0x20000780 0xd00715fd 0x040007c8
0x2440c025 0x04200780 0xd009a805 0x20000780 0x2440c001 0x04200780 0xd009a805 0x20000780
0x2400c001 0x04214680 0x30020001 0xc4100680 0x2400c00d 0x04224680 0x3002060d 0xc4100680
0x2000ca01 0x04200680 0xd00e0025 0xa0c00680 0x2000ca0d 0x0420c680 0xd00e0601 0xa0c00680
0x1000f825 0x0403c100 0x861ffe03 0x00000000 0x1000f801 0x0403c100 0x861ffe03 0x00000000
0x301f1211 0xec100780 0x30011201 0xc4100780 0x301f001d 0xec100780 0x30010025 0xc4100780
0xd0000801 0x04008780 0x00000e05 0xc0000780 0x1100f20c 0x10008200 0xd0090e1d 0x04008780
0x30840001 0xac400780 0x04001601 0xe4200780 0x200af003 0x00000780 0x20000201 0x04000780
0x861ffe03 0x00000000 0xd0095805 0x20000780 0x00020005 0xc0000780 0x30840e01 0xac400780
0x1000d215 0x0423c780 0x20018c11 0x00000003 0x04001601 0xe4200780 0x861ffe03 0x00000000
0x1000d201 0x0423c780 0x3505e014 0x40011024 0xd0095805 0x20000780 0x1000d20d 0x0423c780
0x60001225 0x00024780 0x400b1029 0x00000780 0x20018a05 0x00000003 0x1000d201 0x0423c780
0x30101225 0xc4100780 0x600a1229 0x00028780 0x3503e00c 0x4001041c 0x60000625 0x0001c780
0x200003fd 0x040087c8 0x60001025 0x00024780 0x4007041d 0x00000780 0x30101225 0xc4100780
0x30101401 0xc4100780 0x307cd3fd 0x6c20c7d8 0x6006061d 0x0001c780 0x200005fd 0x040187c8
0x600a1025 0x00000100 0x1009f003 0x00001280 0x60000401 0x00024780 0x30100e09 0xc4100780
0x1000d201 0x0423c780 0x40050005 0x00000780 0x307cd3fd 0x6c20c7d8 0x60060401 0x00008100
0x60040205 0x00004780 0x30100205 0xc4100780 0x1009f003 0x00001280 0x2101f205 0x00000003
0x60040011 0x00004780 0x200b8801 0x00000003 0x40010c05 0x00018780 0x200b8209 0x00000003
0x00020005 0xc0000780 0xa009e003 0x00000000 0x00020405 0xc0000780 0xa009e003 0x00000000
0x2000d205 0x04210780 0x20018811 0x00000003 0x2000d209 0x04204780 0x20018205 0x00000003
0x3606c201 0xec200780 0x300109fd 0x6c0147c8 0x3605c20d 0xec200780 0x300203fd 0x6c0147c8
0x20001225 0x04000780 0x10099003 0x00000280 0x20000001 0x0400c780 0x10099003 0x00000280
0xf0000001 0xe0000002 0x861ffe03 0x00000000 0xf0000001 0xe0000002 0x300609fd 0x640107c8
0x00000e05 0xc0000780 0x04001601 0xe4224780 0x30850bfd 0x6440c2c8 0x30000003 0x00000100
0x861ffe03 0x00000000 0x300607fd 0x640107c8 0x2101f009 0x00000003 0x100f8005 0x00000003
0x308505fd 0x6440c2c8 0x30000003 0x00000100 0x30020205 0xc4000780 0x40034e0d 0x00200780
0x2101f00d 0x00000003 0x100f8001 0x00000003 0x3010060d 0xc4100780 0x30020a09 0xc4000780
0x30030005 0xc4000780 0x40034e01 0x00200780 0x60024e0d 0x0020c780 0x20069004 0x20028608
0x30100001 0xc4100780 0x60024e05 0x00200780 0x20000205 0x04008780 0x30020205 0xc4100780
0x3003040d 0xc4000780 0x20001011 0x04018780 0x2000c805 0x04204780 0xd00e0201 0xa0c00780
0x30040c01 0xc4100780 0x20038204 0x20008400 0x30000003 0x00000780 0xa0000625 0x04114780
0x20000805 0x04004780 0x00020005 0xc0000780 0xa0001229 0x44004780 0xa000002d 0x04114780
0x30020205 0xc4100780 0x1500f600 0x2101e804 0x90001431 0x00000780 0xa0001629 0x44064780
0xd00e0201 0xa0c00781 0x203e9831 0x0fffffff 0xc00c1429 0x0000c7c0
0xa0001429 0x84064780 0x40152435 0x00000780
0x60142635 0x00034780 0x30101a35 0xc4100780
0x60142435 0x00034780 0x20401635 0x04034780
0xa0001a35 0x44064780 0xc00c1a31 0x0000c7c0
0xa0001831 0x84064780 0x20001429 0x04030780
0x40122a31 0x00000780 0x60132831 0x00030780
0x30101831 0xc4100780 0x60122831 0x00030780
0x3000182d 0x0402c780 0x300b1225 0x6400c780
0xd0000601 0x04008780 0x301f0001 0xe4100780
0x30001229 0x04028780 0xa0000025 0x2c014780
0xd00a1225 0x04008780 0x307c07fd 0x6c0147c8
0x20000001 0x04024780 0xd0030001 0x0402c500
0x30000003 0x00000780 0xf0000001 0xe0000001
} }
} }
code { code {