optimizations

This commit is contained in:
chudov
2009-10-08 17:22:16 +00:00
parent 9a1a1956af
commit f3d0c20c6e
3 changed files with 341 additions and 524 deletions

View File

@@ -1161,10 +1161,11 @@ namespace CUETools.Codecs.FlaCuda
cuda.SetParameter(task.cudaComputeLPC, 1 * sizeof(uint), (uint)task.nResidualTasksPerChannel);
cuda.SetParameter(task.cudaComputeLPC, 2 * sizeof(uint), (uint)task.cudaAutocorOutput.Pointer);
cuda.SetParameter(task.cudaComputeLPC, 3 * sizeof(uint), (uint)eparams.max_prediction_order);
cuda.SetParameter(task.cudaComputeLPC, 4 * sizeof(uint), (uint)task.nTasksPerWindow);
cuda.SetParameter(task.cudaComputeLPC, 5 * sizeof(uint), (uint)autocorPartCount);
cuda.SetParameterSize(task.cudaComputeLPC, 6U * sizeof(uint));
cuda.SetFunctionBlockShape(task.cudaComputeLPC, 32, 8, 1);
cuda.SetParameter(task.cudaComputeLPC, 4 * sizeof(uint), (uint)task.cudaLPCData.Pointer);
cuda.SetParameter(task.cudaComputeLPC, 5 * sizeof(uint), (uint)_windowcount);
cuda.SetParameter(task.cudaComputeLPC, 6 * sizeof(uint), (uint)autocorPartCount);
cuda.SetParameterSize(task.cudaComputeLPC, 7U * sizeof(uint));
cuda.SetFunctionBlockShape(task.cudaComputeLPC, 32, 1, 1);
cuda.SetParameter(task.cudaComputeLPCLattice, 0, (uint)task.cudaResidualTasks.Pointer);
cuda.SetParameter(task.cudaComputeLPCLattice, 1 * sizeof(uint), (uint)task.nResidualTasksPerChannel);
@@ -1174,6 +1175,15 @@ namespace CUETools.Codecs.FlaCuda
cuda.SetParameterSize(task.cudaComputeLPCLattice, 5U * sizeof(uint));
cuda.SetFunctionBlockShape(task.cudaComputeLPCLattice, 256, 1, 1);
cuda.SetParameter(task.cudaQuantizeLPC, 0, (uint)task.cudaResidualTasks.Pointer);
cuda.SetParameter(task.cudaQuantizeLPC, 1 * sizeof(uint), (uint)task.nResidualTasksPerChannel);
cuda.SetParameter(task.cudaQuantizeLPC, 2 * sizeof(uint), (uint)task.nTasksPerWindow);
cuda.SetParameter(task.cudaQuantizeLPC, 3 * sizeof(uint), (uint)_windowcount);
cuda.SetParameter(task.cudaQuantizeLPC, 4 * sizeof(uint), (uint)task.cudaLPCData.Pointer);
cuda.SetParameter(task.cudaQuantizeLPC, 5 * sizeof(uint), (uint)eparams.max_prediction_order);
cuda.SetParameterSize(task.cudaQuantizeLPC, 6U * sizeof(uint));
cuda.SetFunctionBlockShape(task.cudaQuantizeLPC, 32, 8, 1);
cuda.SetParameter(cudaEstimateResidual, sizeof(uint) * 0, (uint)task.cudaResidualOutput.Pointer);
cuda.SetParameter(cudaEstimateResidual, sizeof(uint) * 1, (uint)task.cudaSamples.Pointer);
cuda.SetParameter(cudaEstimateResidual, sizeof(uint) * 2, (uint)task.cudaResidualTasks.Pointer);
@@ -1246,6 +1256,7 @@ namespace CUETools.Codecs.FlaCuda
cuda.LaunchAsync(task.cudaFindWastedBits, channelsCount * task.frameCount, 1, task.stream);
cuda.LaunchAsync(task.cudaComputeAutocor, autocorPartCount, task.nAutocorTasksPerChannel * channelsCount * task.frameCount, task.stream);
cuda.LaunchAsync(task.cudaComputeLPC, task.nAutocorTasksPerChannel, channelsCount * task.frameCount, task.stream);
cuda.LaunchAsync(task.cudaQuantizeLPC, 1, channelsCount * task.frameCount, task.stream);
}
cuda.LaunchAsync(cudaEstimateResidual, residualPartCount, task.nResidualTasksPerChannel * channelsCount * task.frameCount / threads_y, task.stream);
cuda.LaunchAsync(task.cudaChooseBestMethod, 1, channelsCount * task.frameCount, task.stream);
@@ -1964,6 +1975,7 @@ namespace CUETools.Codecs.FlaCuda
public CUfunction cudaComputeAutocor;
public CUfunction cudaComputeLPC;
public CUfunction cudaComputeLPCLattice;
public CUfunction cudaQuantizeLPC;
public CUfunction cudaEstimateResidual;
public CUfunction cudaEstimateResidual8;
public CUfunction cudaEstimateResidual12;
@@ -1979,6 +1991,7 @@ namespace CUETools.Codecs.FlaCuda
public CUfunction cudaFindPartitionOrder;
public CUdeviceptr cudaSamplesBytes;
public CUdeviceptr cudaSamples;
public CUdeviceptr cudaLPCData;
public CUdeviceptr cudaResidual;
public CUdeviceptr cudaPartitions;
public CUdeviceptr cudaRiceParams;
@@ -2016,10 +2029,12 @@ namespace CUETools.Codecs.FlaCuda
samplesBufferLen = sizeof(int) * FlaCudaWriter.MAX_BLOCKSIZE * channelCount;
int partitionsLen = sizeof(int) * (30 << 8) * channelCount * FlaCudaWriter.maxFrames;
int riceParamsLen = sizeof(int) * (4 << 8) * channelCount * FlaCudaWriter.maxFrames;
int lpcDataLen = sizeof(float) * 32 * 33 * lpc.MAX_LPC_WINDOWS * channelCount * FlaCudaWriter.maxFrames;
cudaSamplesBytes = cuda.Allocate((uint)samplesBufferLen / 2);
cudaSamples = cuda.Allocate((uint)samplesBufferLen);
cudaResidual = cuda.Allocate((uint)samplesBufferLen);
cudaLPCData = cuda.Allocate((uint)lpcDataLen);
cudaPartitions = cuda.Allocate((uint)partitionsLen);
cudaRiceParams = cuda.Allocate((uint)riceParamsLen);
cudaBestRiceParams = cuda.Allocate((uint)riceParamsLen / 4);
@@ -2057,6 +2072,7 @@ namespace CUETools.Codecs.FlaCuda
cudaChannelDecorr2 = cuda.GetModuleFunction("cudaChannelDecorr2");
cudaFindWastedBits = cuda.GetModuleFunction("cudaFindWastedBits");
cudaComputeLPC = cuda.GetModuleFunction("cudaComputeLPC");
cudaQuantizeLPC = cuda.GetModuleFunction("cudaQuantizeLPC");
cudaComputeLPCLattice = cuda.GetModuleFunction("cudaComputeLPCLattice");
cudaEstimateResidual = cuda.GetModuleFunction("cudaEstimateResidual");
cudaEstimateResidual8 = cuda.GetModuleFunction("cudaEstimateResidual8");
@@ -2081,6 +2097,7 @@ namespace CUETools.Codecs.FlaCuda
{
cuda.Free(cudaSamples);
cuda.Free(cudaSamplesBytes);
cuda.Free(cudaLPCData);
cuda.Free(cudaResidual);
cuda.Free(cudaPartitions);
cuda.Free(cudaAutocorOutput);

View File

@@ -224,175 +224,95 @@ extern "C" __global__ void cudaComputeLPC(
int taskCount, // tasks per block
float*autoc,
int max_order, // should be <= 32
int taskCount2, // tasks per window function, should be <= max_order
int partCount // should be <= blockDim?
float *lpcs,
int windowCount,
int partCount
)
{
__shared__ struct {
FlaCudaSubframeData task;
union
{
volatile float parts[256];
volatile int tmpi[256];
};
volatile float lpc[33*16];
volatile float parts[32];
volatile float ldr[32];
volatile float gen1[32];
volatile float error[32];
volatile float autoc[33];
volatile float error[64];
volatile float order[64];
//volatile float reff[32];
//int cbits;
volatile int lpcOffs;
volatile int autocOffs;
} shared;
const int tid = threadIdx.x + threadIdx.y * 32;
const int tid = threadIdx.x;// + threadIdx.y * 32;
// fetch task data
if (tid < sizeof(shared.task) / sizeof(int))
((int*)&shared.task)[tid] = ((int*)(tasks + blockIdx.y * taskCount))[tid];
__syncthreads();
if (tid == 0)
{
shared.lpcOffs = (blockIdx.x + blockIdx.y * windowCount) * (max_order + 1) * 32;
shared.autocOffs = (blockIdx.x + blockIdx.y * windowCount) * (max_order + 1) * partCount;
}
//__syncthreads();
// add up autocorrelation parts
for (int order = threadIdx.y; order <= max_order; order += 8)
// for (int order = threadIdx.x; order <= max_order; order += 32)
// {
//float sum = 0.0f;
//for (int pos = 0; pos < partCount; pos++)
// sum += autoc[shared.autocOffs + pos * (max_order + 1) + order];
//shared.autoc[order] = sum;
// }
for (int order = 0; order <= max_order; order ++)
{
shared.parts[tid] = 0.0f;
for (int pos = threadIdx.x; pos < partCount; pos += 32)
shared.parts[tid] += autoc[((blockIdx.y * gridDim.x + blockIdx.x) * partCount + pos) * (max_order + 1) + order];
shared.parts[tid] += autoc[shared.autocOffs + pos * (max_order + 1) + order];
shared.parts[tid] = shared.parts[tid] + shared.parts[tid + 8] + shared.parts[tid + 16] + shared.parts[tid + 24];
shared.parts[tid] = shared.parts[tid] + shared.parts[tid + 2] + shared.parts[tid + 4] + shared.parts[tid + 6];
if (threadIdx.x == 0)
shared.autoc[order] = shared.parts[tid] + shared.parts[tid + 1];
}
__syncthreads();
//__syncthreads();
// Compute LPC using Schur and Levinson-Durbin recursion
if (threadIdx.y == 0)
{
float gen0 = shared.gen1[tid] = shared.autoc[tid+1];
shared.ldr[tid] = 0.0f;
float gen0 = shared.gen1[threadIdx.x] = shared.autoc[threadIdx.x+1];
shared.ldr[threadIdx.x] = 0.0f;
float error = shared.autoc[0];
for (int order = 0; order < max_order; order++)
{
// Schur recursion
float reff = -shared.gen1[0] / error;
//if (tid == 0) shared.reff[order] = reff;
error += shared.gen1[0] * reff;
//error *= (1 - reff * reff);
if (tid < max_order - 1 - order)
error += shared.gen1[0] * reff; // Equivalent to error *= (1 - reff * reff);
if (threadIdx.x < max_order - 1 - order)
{
float gen1 = shared.gen1[tid + 1] + reff * gen0;
gen0 += shared.gen1[tid + 1] * reff;
shared.gen1[tid] = gen1;
float gen1 = shared.gen1[threadIdx.x + 1] + reff * gen0;
gen0 += shared.gen1[threadIdx.x + 1] * reff;
shared.gen1[threadIdx.x] = gen1;
}
// Levinson-Durbin recursion
shared.ldr[tid] += (tid < order) * reff * shared.ldr[order - 1 - tid] + (tid == order) * reff;
shared.lpc[((order * (order + 1)) >> 1) + tid] = -shared.ldr[tid];
// Store prediction error
if (threadIdx.x == 0)
shared.error[order] = error;
}
shared.order[tid] = tid < max_order ? tid : max_order - 1;
shared.order[tid + 32] = 0;
if (taskCount2 < max_order)
{
// Select best orders based on something similar to Schwartz's Criterion
shared.error[tid] = tid < max_order ? __logf(shared.error[tid]) + (tid * 0.01f) : __logf(shared.error[0]) + 1;
shared.error[tid + 32] = __logf(shared.error[0]) + 1;
for(int size = 2; size < 32; size <<= 1){
//Bitonic merge
int ddd = (threadIdx.x & (size / 2)) == 0;
for(int stride = size / 2; stride > 0; stride >>= 1){
int pos = 2 * threadIdx.x - (threadIdx.x & (stride - 1));
if ((shared.error[pos] >= shared.error[pos + stride]) == ddd)
{
float t = shared.error[pos];
shared.error[pos] = shared.error[pos + stride];
shared.error[pos + stride] = t;
int t1 = shared.order[pos];
shared.order[pos] = shared.order[pos + stride];
shared.order[pos + stride] = t1;
}
}
}
// Levinson-Durbin recursion
shared.ldr[threadIdx.x] += (threadIdx.x < order) * reff * shared.ldr[order - 1 - threadIdx.x] + (threadIdx.x == order) * reff;
//ddd == dir for the last bitonic merge step
{
for(int stride = 16; stride > 0; stride >>= 1){
int pos = 2 * threadIdx.x - (threadIdx.x & (stride - 1));
if (shared.error[pos] >= shared.error[pos + stride])
{
float t = shared.error[pos];
shared.error[pos] = shared.error[pos + stride];
shared.error[pos + stride] = t;
int t1 = shared.order[pos];
shared.order[pos] = shared.order[pos + stride];
shared.order[pos + stride] = t1;
}
}
}
// float l1 = shared.error[tid];
// #pragma unroll 0
// for (int sh = 4; sh >= 0; sh --)
// {
//float l2 = shared.error[threadIdx.x + (1 << sh)];
//shared.order[threadIdx.x] = shared.order[threadIdx.x + ((l2 < l1) << sh)];
//shared.error[threadIdx.x] = l1 = min(l1, l2);
// }
}
}
__syncthreads();
// Quantization
for (int i = threadIdx.y; i < taskCount2; i += 8)
//for (int precision = 0; precision < 1; precision++)//precisions; precision++)
{
int order = shared.order[i];
float lpc = threadIdx.x <= order ? shared.lpc[((order * (order + 1)) >> 1) + order - threadIdx.x] : 0.0f;
// get 15 bits of each coeff
int coef = __float2int_rn(lpc * (1 << 15));
// remove sign bits
shared.tmpi[tid] = coef ^ (coef >> 31);
// OR reduction
shared.tmpi[tid] = shared.tmpi[tid] | shared.tmpi[tid + 8] | shared.tmpi[tid + 16] | shared.tmpi[tid + 24];
shared.tmpi[tid] = shared.tmpi[tid] | shared.tmpi[tid + 2] | shared.tmpi[tid + 4] | shared.tmpi[tid + 6];
//SUM32(shared.tmpi,tid,|=);
// choose precision
//int cbits = max(3, min(10, 5 + (shared.task.abits >> 1))); // - __float2int_rn(shared.PE[order - 1])
int cbits = max(3, min(min(13 - (shared.task.blocksize <= 2304) - (shared.task.blocksize <= 1152) - (shared.task.blocksize <= 576), shared.task.abits), __clz(order) + 1 - shared.task.abits));
// calculate shift based on precision and number of leading zeroes in coeffs
int shift = max(0,min(15, __clz(shared.tmpi[threadIdx.y * 32] | shared.tmpi[threadIdx.y * 32 + 1]) - 18 + cbits));
//if (shared.task.abits + 32 - __clz(order) < shift
//int shift = max(0,min(15, (shared.task.abits >> 2) - 14 + __clz(shared.tmpi[threadIdx.x & ~31]) + ((32 - __clz(order))>>1)));
// quantize coeffs with given shift
coef = max(-(1 << (cbits - 1)), min((1 << (cbits - 1)) -1, __float2int_rn(lpc * (1 << shift))));
// error correction
//shared.tmp[threadIdx.x] = (threadIdx.x != 0) * (shared.arp[threadIdx.x - 1]*(1 << shared.task.shift) - shared.task.coefs[threadIdx.x - 1]);
//shared.task.coefs[threadIdx.x] = max(-(1 << (shared.task.cbits - 1)), min((1 << (shared.task.cbits - 1))-1, __float2int_rn((shared.arp[threadIdx.x]) * (1 << shared.task.shift) + shared.tmp[threadIdx.x])));
// remove sign bits
shared.tmpi[tid] = coef ^ (coef >> 31);
// OR reduction
shared.tmpi[tid] = shared.tmpi[tid] | shared.tmpi[tid + 8] | shared.tmpi[tid + 16] | shared.tmpi[tid + 24];
shared.tmpi[tid] = shared.tmpi[tid] | shared.tmpi[tid + 2] | shared.tmpi[tid + 4] | shared.tmpi[tid + 6];
//SUM32(shared.tmpi,tid,|=);
// calculate actual number of bits (+1 for sign)
cbits = 1 + 32 - __clz(shared.tmpi[threadIdx.y * 32] | shared.tmpi[threadIdx.y * 32 + 1]);
// output shift, cbits and output coeffs
int taskNo = blockIdx.y * taskCount + blockIdx.x * taskCount2 + i;
if (threadIdx.x == 0)
tasks[taskNo].data.shift = shift;
if (threadIdx.x == 0)
tasks[taskNo].data.cbits = cbits;
if (threadIdx.x == 0)
tasks[taskNo].data.residualOrder = order + 1;
// Output coeffs
if (threadIdx.x <= order)
tasks[taskNo].coefs[threadIdx.x] = coef;
lpcs[shared.lpcOffs + order * 32 + threadIdx.x] = -shared.ldr[order - threadIdx.x];
}
// Output prediction error estimates
if (threadIdx.x < max_order)
lpcs[shared.lpcOffs + max_order * 32 + threadIdx.x] = shared.error[threadIdx.x];
}
}
extern "C" __global__ void cudaQuantizeLPC(
FlaCudaSubframeTask *tasks,
int taskCount, // tasks per block
int taskCountLPC, // LPC tasks per block
int taskCountLPC, // tasks per set of coeffs
int windowCount, // sets of coeffs per block
float*lpcs,
int max_order // should be <= 32
@@ -401,7 +321,9 @@ extern "C" __global__ void cudaQuantizeLPC(
__shared__ struct {
FlaCudaSubframeData task;
volatile int tmpi[256];
volatile int order[256];
volatile int order[128];
volatile int offset[128];
volatile int index[256];
volatile float error[256];
} shared;
const int tid = threadIdx.x + threadIdx.y * 32;
@@ -411,34 +333,34 @@ extern "C" __global__ void cudaQuantizeLPC(
((int*)&shared.task)[tid] = ((int*)(tasks + blockIdx.y * taskCount))[tid];
__syncthreads();
shared.order[tid] = min(max_order - 1, threadIdx.x) + min(threadIdx.y, windowCount - 1) * 32;
shared.error[tid] = 10000.0f + shared.order[tid];
{
int lpcs_offs = (threadIdx.y + blockIdx.y * windowCount) * (max_order + 1) * 32;
shared.index[tid] = min(max_order - 1, threadIdx.x) + min(threadIdx.y >> 1, windowCount - 1) * 32;
shared.error[tid] = 10000.0f + shared.index[tid];
// Select best orders based on Akaike's Criteria
if ((threadIdx.y & 1) == 0 && (threadIdx.y >> 1) < windowCount)
{
// Load prediction error estimates
if (threadIdx.y < windowCount && threadIdx.x < max_order)
if (threadIdx.x < max_order)
{
int lpcs_offs = ((threadIdx.y >> 1) + blockIdx.y * windowCount) * (max_order + 1) * 32;
shared.error[tid] = __logf(lpcs[lpcs_offs + max_order * 32 + threadIdx.x]) + (threadIdx.x * 0.01f);
__syncthreads();
}
// Sort using bitonic sort
for(int size = 2; size < 64; size <<= 1){
//Bitonic merge
int ddd = (tid & (size / 2)) == 0;
for(int stride = size / 2; stride > 0; stride >>= 1){
__syncthreads();
int pos = 2 * tid - (tid & (stride - 1));
//__syncthreads();
int pos = threadIdx.y * 32 + 2 * threadIdx.x - (threadIdx.x & (stride - 1));
if ((shared.error[pos] >= shared.error[pos + stride]) == ddd)
{
float t = shared.error[pos];
shared.error[pos] = shared.error[pos + stride];
shared.error[pos + stride] = t;
int t1 = shared.order[pos];
shared.order[pos] = shared.order[pos + stride];
shared.order[pos + stride] = t1;
int t1 = shared.index[pos];
shared.index[pos] = shared.index[pos + stride];
shared.index[pos + stride] = t1;
}
}
}
@@ -446,30 +368,34 @@ extern "C" __global__ void cudaQuantizeLPC(
//ddd == dir for the last bitonic merge step
{
for(int stride = 32; stride > 0; stride >>= 1){
__syncthreads();
int pos = 2 * tid - (tid & (stride - 1));
//__syncthreads();
int pos = threadIdx.y * 32 + 2 * threadIdx.x - (threadIdx.x & (stride - 1));
if (shared.error[pos] >= shared.error[pos + stride])
{
float t = shared.error[pos];
shared.error[pos] = shared.error[pos + stride];
shared.error[pos + stride] = t;
int t1 = shared.order[pos];
shared.order[pos] = shared.order[pos + stride];
shared.order[pos + stride] = t1;
}
int t1 = shared.index[pos];
shared.index[pos] = shared.index[pos + stride];
shared.index[pos + stride] = t1;
}
}
}
if (threadIdx.x < taskCountLPC)
{
shared.order[(threadIdx.y >> 1) * taskCountLPC + threadIdx.x] = shared.index[tid] & 31;
shared.offset[(threadIdx.y >> 1) * taskCountLPC + threadIdx.x] = (shared.index[tid] >> 5) + blockIdx.y * windowCount;
}
}
__syncthreads();
// Quantization
for (int i = threadIdx.y; i < taskCountLPC; i += 8)
for (int i = threadIdx.y; i < taskCountLPC * windowCount; i += 8)
//for (int precision = 0; precision < 1; precision++)//precisions; precision++)
{
int order = shared.order[i] & 31;
int lpcs_offs = ((shared.order[i] >> 5) + blockIdx.y * windowCount) * (max_order + 1) * 32;
float lpc = threadIdx.x <= order ? lpcs[lpcs_offs + order * 32 + order - threadIdx.x] : 0.0f;
int order = shared.order[i];
float lpc = threadIdx.x <= order ? lpcs[(shared.offset[i] * (max_order + 1) + order) * 32 + threadIdx.x] : 0.0f;
// get 15 bits of each coeff
int coef = __float2int_rn(lpc * (1 << 15));
// remove sign bits

View File

@@ -292,8 +292,8 @@ code {
code {
name = cudaQuantizeLPC
lmem = 0
smem = 3176
reg = 13
smem = 4200
reg = 11
bar = 1
const {
segname = const
@@ -301,7 +301,7 @@ code {
offset = 0
bytes = 56
mem {
0x000003ff 0x0000000f 0x3c23d70a 0x00000001
0x000003ff 0x0000000f 0x00000001 0x3c23d70a
0xffffffff 0x0000003f 0x0000001f 0x00000900
0x0000000c 0x00000480 0x00000240 0x00000003
0x00000020 0x0000009e
@@ -310,158 +310,165 @@ code {
bincode {
0xd0800205 0x00400780 0xa0000205 0x04000780
0xa0000015 0x04000780 0x30050201 0xc4100780
0x20000a11 0x04000780 0x200a8801 0x00000003
0x00020009 0xc0000780 0x308109fd 0x644107c8
0xa0015003 0x00000000 0x30020809 0xc4100780
0x20000a09 0x04000780 0x200a8401 0x00000003
0x00020009 0xc0000780 0x308105fd 0x644107c8
0xa0015003 0x00000000 0x3002040d 0xc4100780
0x10015003 0x00000280 0x1000ca01 0x0423c780
0x40014e0d 0x00200780 0x3010060d 0xc4100780
0x60004e01 0x0020c780 0x3007000d 0xc4100780
0x30060001 0xc4100780 0x20008600 0x2100e800
0x20000401 0x04000780 0xd00e0001 0x80c00780
0x40014e11 0x00200780 0x30100811 0xc4100780
0x60004e01 0x00210780 0x30070011 0xc4100780
0x30060001 0xc4100780 0x20008800 0x2100e800
0x20000601 0x04000780 0xd00e0001 0x80c00780
0x08000001 0xe4200780 0xf0000001 0xe0000002
0x861ffe03 0x00000000 0x213fee09 0x0fffffff
0x213ff201 0x0fffffff 0x30020209 0xa4000780
0x30000a01 0xa4000780 0x30050409 0xc4100780
0x20000001 0x04008780 0x08022001 0xe4200780
0xd8088005 0x20000780 0xa400c001 0x44214780
0x3001cffd 0x642107c8 0xb0000001 0x0461c403
0x3005d3fd 0x642102c8 0x08042001 0xe4200780
0xa003b003 0x00000000 0x1003b003 0x00000100
0x1000ce01 0x0423c780 0x40014e09 0x00200780
0x30100409 0xc4100780 0x60004e09 0x00208780
0x2101f201 0x00000003 0x20000409 0x04004780
0x4005000d 0x00000780 0x6004020d 0x0000c780
0x3010060d 0xc4100780 0x60040009 0x0000c780
0x3005d201 0xc4300780 0x30050409 0xc4100780
0x20028000 0x20008a00 0x30020001 0xc4100780
0x2000d001 0x04200780 0xd00e0001 0x80c00780
0x90000001 0x60000780 0xa0000a09 0x44004780
0xc0180001 0x03f31723 0xe00a0401 0x03c23d73
0x08042001 0xe4200780 0x30010819 0xc4100782
0x1002801d 0x00000003 0x301f0e01 0xec100780
0xd0830001 0x04400780 0x20000001 0x0401c780
0x30010001 0xec100780 0x307c01fd 0x6c00c7c8
0x10064003 0x00000280 0xd0040009 0x04000780
0x307c05fd 0x6c0087c8 0x861ffe03 0x00000000
0x203f8009 0x0fffffff 0xd0040409 0x04000780
0x20428c0c 0x20008608 0x00020405 0xc0000780
0xd410d00d 0x20000780 0x1c00c009 0x0423c780
0x0002060d 0xc0000780 0xdc10d011 0x20000780
0xb002c00d 0x60218784 0x10000011 0x00000003
0xa0000615 0x08014780 0x10001011 0x20408280
0xd00509fd 0x000087d8 0xa0061003 0x00000000
0x10061003 0x00001280 0xd410d011 0x20000780
0x1000c00d 0x0423c784 0xdc10d011 0x20000780
0x1000c009 0x0423c784 0x0c043401 0xe420c780
0x04043401 0xe4208780 0xd408d011 0x20000780
0x1000c00d 0x0423c784 0xdc08d011 0x20000780
0x1000c009 0x0423c784 0x0c023401 0xe420c780
0x04023401 0xe4208780 0x30010001 0xec100782
0x307c01fd 0x6c0107d8 0x10045003 0x00001280
0x30010e1d 0xc4100780 0x30850ffd 0x6c40c7c8
0x1003d003 0x00000280 0x1020800d 0x00000003
0x861ffe03 0x00000000 0x203f8601 0x0fffffff
0xd0040001 0x04000780 0x20408c08 0x20038400
0x00020005 0xc0000780 0xd410d00d 0x20000780
0x1c00c001 0x0423c780 0x0002040d 0xc0000780
0xdc10d011 0x20000780 0xb000c1fd 0x602187cc
0xa0080003 0x00000000 0x10080003 0x00000100
0xd410d011 0x20000780 0x1000c009 0x0423c784
0xdc10d011 0x20000780 0x1000c001 0x0423c784
0x0c043401 0xe4208780 0x04043401 0xe4200780
0xd408d011 0x20000780 0x1000c009 0x0423c784
0xdc08d011 0x20000780 0x1000c001 0x0423c784
0x0c023401 0xe4208780 0x04023401 0xe4200780
0x3001060d 0xec100782 0x307c07fd 0x6c0107c8
0x10068003 0x00000280 0x861ffe03 0x00000000
0x3001cdfd 0x6c20c7c8 0x10000211 0x0403c780
0x30000003 0x00000280 0xa0004e09 0x04200780
0x1000ce01 0x0423c780 0x4004020d 0x00000780
0x200a8219 0x00000003 0x3010060d 0xc4100780
0x00020c0d 0xc0000780 0x60040019 0x0000c780
0x307c0bfd 0x640087c8 0x2101f20d 0x00000003
0xdc088005 0x20000780 0x3405c001 0xec300780
0x20000c01 0x04000780 0x40010c1d 0x00000780
0x60000e21 0x0001c780 0xd486c01d 0x04600780
0x30101021 0xc4100780 0x30050e25 0x640187e0
0x60000c01 0x00020780 0xa00013fd 0x0c0147d8
0xa00a5003 0x00000000 0x30050001 0xc4100780
0x100a4003 0x00002100 0x30050e21 0xc4100780
0x20088000 0x20008e00 0x20400001 0x04014780
0x861ffe03 0x00000000 0x30010211 0xe4100780
0x213fee0d 0x0fffffff 0x213ff201 0x0fffffff
0x3003080d 0xa4000780 0x30000a01 0xa4000780
0x3005060d 0xc4100780 0x20000001 0x0400c780
0x08042001 0xe4200780 0xd8108005 0x20000780
0xd082020d 0x04400780 0xa400c001 0x44214780
0x307c07fd 0x640087c8 0xb0000001 0x0461c403
0x3004cffd 0x642102c8 0x08062001 0xe4200780
0xa0096003 0x00000000 0x10096003 0x00000100
0x3005d3fd 0x6420c7c8 0xa0040003 0x00000000
0x10040003 0x00000280 0x1000ce01 0x0423c780
0x40014e0d 0x00200780 0x3010060d 0xc4100780
0x60004e0d 0x0020c780 0x2101f201 0x00000003
0x2000060d 0x04010780 0x40070019 0x00000780
0x60060219 0x00018780 0x30100c19 0xc4100780
0x6006000d 0x00018780 0x3005d201 0xc4300780
0x3005060d 0xc4100780 0x20038000 0x20008a00
0x30020001 0xc4100780 0x2000d001 0x04200780
0xd00e0021 0x80c00780 0x100a5003 0x00000780
0x1000f821 0x0403c780 0xd8010005 0x20000782
0xc0001001 0x04700003 0xa0000001 0x8c004780
0x301f0025 0xec100780 0xd0090001 0x04008780
0x08002001 0xe4200780 0x1400d025 0x0423c780
0x1800f001 0x0423c780 0xd409c025 0x04204780
0xd800e001 0x04204780 0xd0090001 0x04004780
0xd00e0001 0x80c00780 0x90000001 0x60000780
0xa0000a0d 0x44004780 0xc0180001 0x03f31723
0xe00a0601 0x03c23d73 0x08062001 0xe4200780
0x30040201 0xc4100782 0x20000001 0x04014780
0x10028021 0x00000003 0x3001001d 0xc4100780
0x301f1001 0xec100780 0xd0820001 0x04400780
0x20000001 0x04020780 0x30010001 0xec100780
0x307c01fd 0x6c00c7c8 0x1006a003 0x00000280
0xd002000d 0x04000780 0x307c07fd 0x6c0087c8
0x203f800d 0x0fffffff 0xd005060d 0x04000780
0x20438e18 0x20008c0c 0x00020605 0xc0000780
0xd418d00d 0x20000780 0x1c00c00d 0x0423c780
0x00020c0d 0xc0000780 0xdc18d011 0x20000780
0xb003c019 0x60218784 0x10000019 0x00000003
0xa0000c1d 0x08014780 0x10001019 0x20408280
0xd0070dfd 0x000087d8 0xa0067003 0x00000000
0x10067003 0x00001280 0xd418d011 0x20000780
0x1000c019 0x0423c784 0xdc18d011 0x20000780
0x1000c00d 0x0423c784 0x0c063401 0xe4218780
0x04063401 0xe420c780 0xd410d011 0x20000780
0x1000c019 0x0423c784 0xdc10d011 0x20000780
0x1000c00d 0x0423c784 0x0c043401 0xe4218780
0x04043401 0xe420c780 0x30010001 0xec100782
0x307c01fd 0x6c0107d8 0x1004c003 0x00001280
0x30011021 0xc4100780 0x308511fd 0x6c40c7c8
0x10044003 0x00000280 0x1020800d 0x00000003
0x203f8601 0x0fffffff 0xd0050001 0x04000780
0x20408e08 0x20038400 0x00020005 0xc0000780
0xd418d00d 0x20000780 0x1c00c001 0x0423c780
0x0002040d 0xc0000780 0xdc18d011 0x20000780
0xb000c1fd 0x602187cc 0xa0085003 0x00000000
0x10085003 0x00000100 0xd418d011 0x20000780
0x1000c009 0x0423c784 0xdc18d011 0x20000780
0x1000c001 0x0423c784 0x0c063401 0xe4208780
0x04063401 0xe4200780 0xd410d011 0x20000780
0x1000c009 0x0423c784 0xdc10d011 0x20000780
0x1000c001 0x0423c784 0x0c043401 0xe4208780
0x04043401 0xe4200780 0x3001060d 0xec100782
0x307c07fd 0x6c0107c8 0x1006e003 0x00000280
0x3005cdfd 0x6420c7c8 0x10096003 0x00000280
0x6004cc09 0x60214780 0x1000ce01 0x0423c780
0xd810800d 0x20000780 0x00020405 0xc0000780
0x40014e0d 0x00200780 0xdc86c009 0x04600780
0x3010060d 0xc4100780 0x04023401 0xe4208780
0x60004e01 0x0020c780 0x3c05c009 0xec300780
0x20000001 0x04008780 0x04033401 0xe4200780
0xf0000001 0xe0000002 0x861ffe03 0x00000000
0x1100ec00 0x1100ee08 0x4005000d 0x00000780
0x6004020d 0x0000c780 0x3010060d 0xc4100780
0x60040019 0x0000c780 0x30010dfd 0x6c00c7c8
0x10000209 0x0403c780 0x30000003 0x00000280
0x200a8201 0x00000003 0x0002000d 0xc0000780
0x307c0bfd 0x640087c8 0xdc088005 0x20000780
0x3405c001 0x642187e0 0xa00001fd 0x0c0147d8
0xa00b8003 0x00000000 0x1400c00d 0x0423c780
0x100b7003 0x00002100 0xdc0c8005 0x20000780
0x2101f211 0x00000003 0x1400c001 0x0423c780
0x4009001d 0x00000780 0x6008021d 0x0001c780
0x30100e1d 0xc4100780 0x60080001 0x0001c780
0x20000001 0x0400c780 0x30050001 0xc4100780
0x20000a01 0x04000780 0x30020001 0xc4100780
0x2000d001 0x04200780 0xd00e0011 0x80c00780
0x100b8003 0x00000780 0x1000f811 0x0403c780
0xd8010005 0x20000782 0xc0000801 0x04700003
0xa0000001 0x8c004780 0x301f001d 0xec100780
0xd0070001 0x04008780 0x08002001 0xe4200780
0x1400d01d 0x0423c780 0x1800f001 0x0423c780
0xd407c01d 0x04204780 0xd800e001 0x04204780
0xd0070001 0x04004780 0x08002001 0xe4200780
0x1800e401 0x0423c780 0x1800ec1d 0x0423c780
0xd800e001 0x04204780 0xd807e81d 0x04204780
0xd0070001 0x04004780 0x307c07fd 0x6c0087e8
0x08002001 0xe4200780 0xa0000601 0x44066500
0x30170001 0xec102500 0x3100001d 0x04436500
0x1000181d 0x2440e280 0x00070205 0xc0000780
0x1400f601 0x0423c780 0xd400f401 0x042047e0
0xa0000001 0x44066680 0x30170001 0xec102680
0x31000001 0x04436680 0x10001801 0x2440e100
0x3087e3fd 0x6c60c7e8 0x3089e225 0x6c60c780
0x100d8021 0x00000003 0xd0821225 0x04400780
0x10001021 0x2440e280 0x308ae229 0x6c60c780
0x20401021 0x04024780 0xd0821425 0x04400780
0x3000ec1d 0x0421c780 0x20401021 0x04024780
0x20018e1d 0x00000003 0x3008ec21 0xac200780
0x30080e1d 0xac000780 0x308b0e21 0x8c400780
0x20000001 0x04020780 0x202e8001 0x0fffffff
0x3081001d 0xac400780 0x10018001 0x00000003
0x307c0e1d 0x8c000780 0x30070025 0xc4000780
0x203f9021 0x0fffffff 0xa0001225 0x44014780
0x30080001 0xc4000780 0xc0041211 0x00000780
0x203f8021 0x0fffffff 0xa0000811 0x8c004780
0x30080811 0xac000780 0x30008001 0x00000003
0x30000811 0x8c000780 0x301f0801 0xec100780
0xd0000801 0x04008780 0x08002001 0xe4200780
0xd8010005 0x20000780 0x1400d021 0x0423c780
0x1800f001 0x0423c780 0xd408c021 0x04204780
0xd800e001 0x04204780 0xd0080001 0x04004780
0x08002001 0xe4200780 0x1800e401 0x0423c780
0x1800ec25 0x0423c780 0xd800e001 0x04204780
0xd809e825 0x04204780 0xd0090001 0x04004780
0x307c0ffd 0x6c0087e8 0x08002001 0xe4200780
0xa0000e01 0x44066500 0x30170001 0xec102500
0x31000025 0x04436500 0x10001825 0x2440e280
0x00070205 0xc0000780 0x1400f601 0x0423c780
0xd400f401 0x042047e0 0xa0000001 0x44066680
0x30170001 0xec102680 0x31000001 0x04436680
0x10001801 0x2440e100 0x3087e3fd 0x6c60c7e8
0x3089e22d 0x6c60c780 0x100d8029 0x00000003
0xd083162d 0x04400780 0x10001029 0x2440e280
0x308ae231 0x6c60c780 0x20401429 0x0402c780
0xd083182d 0x04400780 0x3000ec25 0x04224780
0x20401429 0x0402c780 0x20019225 0x00000003
0x300aec29 0xac200780 0x300a1225 0xac000780
0x308b1229 0x8c400780 0x20000001 0x04028780
0x202e8001 0x0fffffff 0x30810025 0xac400780
0x10018001 0x00000003 0x307c1225 0x8c000780
0x3009002d 0xc4000780 0x203f9429 0x0fffffff
0xa000162d 0x44014780 0x300a0001 0xc4000780
0xc0081621 0x00000780 0x203f8029 0x0fffffff
0xa0001021 0x8c004780 0x300a1021 0xac000780
0x30008001 0x00000003 0x30001021 0x8c000780
0x301f1001 0xec100780 0xd0001001 0x04008780
0x08002001 0xe4200780 0xd8010005 0x20000780
0x1400d029 0x0423c780 0x1800f001 0x0423c780
0xd40ac029 0x04204780 0xd800e001 0x04204780
0xd00a0001 0x04004780 0x08002001 0xe4200780
0x1800e401 0x0423c780 0x1800ec29 0x0423c780
0xd800e001 0x04204780 0xd80ae829 0x04204780
0xd00a0001 0x04004780 0x08002001 0xe4200780
0x00070205 0xc0000780 0x1400f601 0x0423c780
0xd400f401 0x042047e0 0xa0000001 0x44066680
0x30170001 0xec102680 0x31000029 0x04436680
0x10001829 0x2440e100 0xa011e003 0x00000000
0x1011e003 0x00000100 0x1000ca01 0x0423c780
0x4005002d 0x00000780 0x6004022d 0x0002c780
0x3010162d 0xc4100780 0x60040001 0x0002c780
0x2000002d 0x04010780 0x30071601 0xc4100780
0x3006162d 0xc4100780 0x200b8000 0x2100e800
0x20088001 0x00000003 0xd00e0025 0xa0c00780
0x1011e003 0x00000100 0x1000ca01 0x0423c780
0x40050025 0x00000780 0x60040225 0x00024780
0x30101225 0xc4100780 0x60040001 0x00024780
0x20000001 0x04010780 0x30070025 0xc4100780
0x30060001 0xc4100780 0x20009200 0x2100e800
0x30219425 0x00000003 0x200c8001 0x00000003
0xd00e0025 0xa0c00780 0x1011e003 0x00000100
0x1000ca01 0x0423c780 0x40050025 0x00000780
0x60040225 0x00024780 0x30101225 0xc4100780
0x60040001 0x00024780 0x20000001 0x04010780
0x30070025 0xc4100780 0x30060001 0xc4100780
0x20001201 0x04000780 0x20018e1d 0x00000003
0x2000c801 0x04200780 0xd00e001d 0xa0c00780
0xf0000001 0xe0000002 0xa012e003 0x00000000
0x1012e003 0x00001100 0x1000ca01 0x0423c780
0x4005001d 0x00000780 0x6004021d 0x0001c780
0x30100e1d 0xc4100780 0x60040001 0x0001c780
0x20000001 0x04010780 0x3007001d 0xc4100780
0x30060001 0xc4100780 0x20000e1d 0x04000780
0x30020a01 0xc4100780 0x2107e81c 0x20078000
0x20008001 0x00000007 0xd00e0021 0xa0c00780
0xdc00400d 0x20000782 0x20088811 0x00000003
0x3004cdfd 0x6c2107d8 0x10090003 0x00001280
0xf0000001 0xe0000001
0x1800ec21 0x0423c780 0xd800e001 0x04204780
0xd808e821 0x04204780 0xd0080001 0x04004780
0x08002001 0xe4200780 0x00070205 0xc0000780
0x1400f601 0x0423c780 0xd400f401 0x042047e0
0xa0000001 0x44066680 0x30170001 0xec102680
0x31000021 0x04436680 0x10001821 0x2440e100
0xa012e003 0x00000000 0x1012e003 0x00000100
0x1000ca01 0x0423c780 0x40014e25 0x00200780
0x30101225 0xc4100780 0x60004e01 0x00224780
0x20000025 0x04008780 0x30071201 0xc4100780
0x30061225 0xc4100780 0x20098000 0x2100e800
0x20088001 0x00000003 0xd00e001d 0xa0c00780
0x1012e003 0x00000100 0x1000ca01 0x0423c780
0x40014e1d 0x00200780 0x30100e1d 0xc4100780
0x60004e01 0x0021c780 0x20000001 0x04008780
0x3007001d 0xc4100780 0x30060001 0xc4100780
0x20008e00 0x2100e800 0x3021901d 0x00000003
0x200c8001 0x00000003 0xd00e001d 0xa0c00780
0x1012e003 0x00000100 0x1000ca01 0x0423c780
0x40014e1d 0x00200780 0x30100e1d 0xc4100780
0x60004e01 0x0021c780 0x20000001 0x04008780
0x3007001d 0xc4100780 0x30060001 0xc4100780
0x20000e01 0x04000780 0x2001860d 0x00000003
0x2000c801 0x04200780 0xd00e000d 0xa0c00780
0xf0000001 0xe0000002 0xa013d003 0x00000000
0x1013d003 0x00001100 0x1000ca01 0x0423c780
0x40014e0d 0x00200780 0x3010060d 0xc4100780
0x60004e01 0x0020c780 0x20000001 0x04008780
0x3007000d 0xc4100780 0x30060001 0xc4100780
0x2000060d 0x04000780 0x30020a01 0xc4100780
0x2103e80c 0x20038000 0x20008001 0x00000007
0xd00e0011 0xa0c00780 0xdc00400d 0x20000782
0x20088409 0x00000003 0x30020dfd 0x6c0107d8
0x100a3003 0x00001280 0xf0000001 0xe0000001
}
}
code {
@@ -1723,229 +1730,96 @@ code {
code {
name = cudaComputeLPC
lmem = 0
smem = 4140
reg = 10
bar = 1
smem = 760
reg = 8
bar = 0
const {
segname = const
segnum = 1
offset = 0
bytes = 64
bytes = 16
mem {
0x000003ff 0x0000000f 0x7e800000 0x3c23d70a
0x3f317218 0x00000001 0xffffffff 0x0000001f
0x00000900 0x0000000c 0x00000480 0x00000240
0x00000003 0x3e800000 0x00000020 0x0000009e
0x0000000f 0x000003ff 0x7e800000 0x3e800000
}
}
bincode {
0xd0800205 0x00400780 0xa0000205 0x04000780
0xa0000011 0x04000780 0x30050201 0xc4100780
0x20000815 0x04000780 0x30810bfd 0x644107c8
0xa0015003 0x00000000 0x00070205 0xc0000780
0x10015003 0x00000280 0x1000ca01 0x0423c780
0x40014e09 0x00200780 0x30100409 0xc4100780
0x60004e01 0x00208780 0x30070009 0xc4100780
0x30060001 0xc4100780 0x20000409 0x04000780
0x30020a01 0xc4100780 0x2102e808 0x20028000
0xd00e0001 0x80c00780 0x00020a09 0xc0000780
0x08001401 0xe4200780 0xf0000001 0xe0000002
0x861ffe03 0x00000000 0x3001cffd 0x6c2047c8
0xa004c003 0x00000000 0x10000209 0x0403c780
0x1004c003 0x00000280 0x200a8a01 0x00000003
0x00020009 0xc0000780 0x3004d3fd 0x6c2107d8
0x307c09fd 0x640087c8 0xa003b003 0x00000000
0x08002001 0xe43f0780 0x1003b003 0x00001100
0xa0004c0d 0x04200780 0x10004e01 0x0023c780
0x6000480d 0x0020c780 0x1000d201 0x0423c780
0x40070019 0x00000780 0x60060219 0x00018780
0x30100c19 0xc4100780 0x60060019 0x00018780
0x2101ee0d 0x00000003 0x20000c01 0x04010780
0x40010c1d 0x00000780 0x60000e1d 0x0001c780
0x30100e1d 0xc4100780 0x60000c1d 0x0001c780
0x20000e1d 0x04008780 0x30020e1d 0xc4100780
0x3007060d 0xc4100780 0x2106f218 0x2107ec1c
0xd00e0e21 0x80c00780 0x20208001 0x00000003
0xb800e021 0x00220780 0x300601fd 0x6c0047e8
0x08002001 0xe4220780 0x20000e1d 0x0400c780
0x10034003 0x00002280 0x1800f001 0x0423c782
0xd801000d 0x20000780 0xb800e001 0x00200780
0xbd006000 0xbd007000 0x08002001 0xe4200780
0x1800e401 0x0423c780 0xb800e001 0x00200780
0xb800e801 0x00200780 0xb800ec01 0x00200780
0x08002001 0xe4200780 0x1800e201 0x0423c680
0x0002040d 0xc0000680 0xb800e001 0x00200680
0x0c06d401 0xe4200680 0x20088409 0x00000003
0x3002cffd 0x6c2187e8 0x1001f003 0x00002280
0xf0000001 0xe0000002 0x861ffe03 0x00000000
0x307c03fd 0x640147c8 0xa00f1003 0x00000000
0x100f1003 0x00000280 0x00020a09 0xc0000780
0xd81b580d 0x20000780 0x1d00e008 0x1d00e000
0x08069401 0xe4208780 0xd01b500d 0x20000780
0x08065401 0xe43f0780 0x307ccffd 0x6c20c7c8
0x1c00c00d 0x0423c780 0x1008a003 0x00000280
0x10288009 0x00000003 0x0000040d 0xc0000780
0x213fee1d 0x0fffffff 0x1000f809 0x0403c780
0xd01a5011 0x20000780 0xb08207fd 0x605107c8
0x10000619 0x0403c780 0xa000c021 0xe4204784
0xc08d1021 0x00400680 0xc08d0c19 0x00400680
0x90000c24 0x20428e18 0xc0091021 0x00000780
0xd01a5011 0x20000780 0x30060bfd 0x6c0187c8
0xe008c00d 0x0020c784 0xd81a5811 0x20000500
0x1000c019 0x0423c504 0xe0001019 0x00018500
0xe008c001 0x00200504 0x08069401 0xe4218500
0x20400419 0x04014780 0x00020c11 0xc0000780
0xd0194811 0x20000784 0x40050825 0x00000780
0x300505fd 0x6c00c7c8 0xc008c019 0x00200784
0x60040a25 0x00024780 0x1000f819 0x0403c280
0x30101225 0xc4100780 0x300505fd 0x6c0147c8
0xb0000c21 0x00020780 0x60040825 0x00024780
0x10000c21 0x0403c280 0xd8195011 0x20000780
0x20000425 0x04024780 0xb000c019 0x00220784
0x30011221 0xec100780 0x08065401 0xe4218780
0x20000a19 0x04020780 0xa000c021 0xe4204784
0x00020c11 0xc0000780 0x00023401 0xe4220784
0x20018409 0x00000003 0x0c070201 0xe420c780
0x3002cffd 0x6c2147c8 0xdc00080d 0x20000780
0x1005e003 0x00000280 0x1008b003 0x00000780
0x213fee1d 0x0fffffff 0xa0000a09 0x44014780
0x3005cffd 0x6c20c7c8 0x10000401 0x0403c780
0xa0000e01 0x44014280 0x1000ce0d 0x0423c780
0x08079601 0xe4200780 0x3003d1fd 0x6c2187c8
0x0807d601 0xe43f0780 0x100f1003 0x00000280
0x3005cffd 0x6c20c7c8 0xa00a2003 0x00000000
0x1009d003 0x00000280 0xd81c580d 0x20000780
0x1c00c001 0x0423c780 0x90000001 0x60000780
0xc0180001 0x03f31723 0xe00a0401 0x03c23d73
0x100a2003 0x00000780 0xd01c580d 0x20000780
0x1c00c009 0x0423c780 0x10008001 0x03f80003
0x90000409 0x60000780 0xe0180401 0x03f31723
0x08071601 0xe4200782 0xd01c580d 0x20000780
0x1c00c009 0x0423c780 0x10008001 0x03f80003
0x90000409 0x60000780 0xe0180401 0x03f31723
0x08075601 0xe4200780 0x30010819 0xc4100780
0x1002801d 0x00000003 0x301f0e01 0xec100780
0xd0850001 0x04400780 0x20000001 0x0401c780
0x30010001 0xec100780 0x307c01fd 0x6c00c7c8
0x100d2003 0x00000280 0xd0040009 0x04000780
0x307c05fd 0x640087c8 0x203f8009 0x0fffffff
0xd0040409 0x04000780 0x20428c08 0x2000840c
0x0002060d 0xc0000780 0xdc1c5811 0x20000780
0x00020409 0xc0000780 0x1000c009 0x0423c784
0xd81c5811 0x20000780 0xb002c00d 0x60218784
0x10000011 0x00000003 0xa0000615 0x08014780
0x10001811 0x20408280 0xd00509fd 0x000087d8
0xa00cf003 0x00000000 0x100cf003 0x00001280
0xdc1c5811 0x20000780 0x1000c00d 0x0423c784
0xd81c5811 0x20000780 0x1000c009 0x0423c784
0x08071601 0xe420c780 0x0c071601 0xe4208780
0xd81e5811 0x20000780 0xa000c00d 0x8c264784
0xdc1e5811 0x20000780 0x1000c009 0x0423c784
0x08079601 0xe4208780 0xa0000609 0x44014780
0x0c079601 0xe4208780 0x30010001 0xec100782
0x307c01fd 0x6c0107d8 0x100b3003 0x00001280
0x30010e1d 0xc4100780 0x30870ffd 0x6c40c7c8
0x100ab003 0x00000280 0x1010800d 0x00000003
0x203f8601 0x0fffffff 0xd0040001 0x04000780
0x20408c00 0x20038008 0x0002040d 0xc0000780
0xdc1c5811 0x20000780 0x00020009 0xc0000780
0x1000c001 0x0423c784 0xd81c5811 0x20000780
0xb000c1fd 0x602187cc 0xa00ee003 0x00000000
0x100ee003 0x00000100 0xdc1c5811 0x20000780
0x1000c009 0x0423c784 0xd81c5811 0x20000780
0x1000c001 0x0423c784 0x08071601 0xe4208780
0x0c071601 0xe4200780 0xd81e5811 0x20000780
0xa000c009 0x8c264784 0xdc1e5811 0x20000780
0x1000c001 0x0423c784 0x08079601 0xe4200780
0xa0000401 0x44014780 0x0c079601 0xe4200780
0x3001060d 0xec100782 0x307c07fd 0x6c0107c8
0x100d6003 0x00000280 0xf0000001 0xe0000002
0x861ffe03 0x00000000 0x3001d1fd 0x6c20c7c8
0x10000201 0x0403c780 0x30000003 0x00000280
0x200a8a09 0x00000003 0x200a8205 0x00000003
0x0002040d 0xc0000780 0x00020209 0xc0000780
0x307c09fd 0x640087c8 0xd81e0811 0x20000780
0xa000c00d 0x8c264784 0x30040605 0x640187e0
0xa00003fd 0x0c0147d8 0xa010d003 0x00000000
0x1010c003 0x00002100 0x20018605 0x00000003
0x40030c09 0x00000780 0x60020e09 0x00008780
0x30100409 0xc4100780 0x60020c05 0x00008780
0x30010205 0xec100780 0x20018604 0x20448204
0x00020211 0xc0000780 0xd008d011 0x20000784
0x1000c005 0x0423c784 0x1010d003 0x00000780
0x1000f805 0x0403c780 0xdc010011 0x20000782
0xc0000209 0x04700003 0xa0000409 0x8c004780
0x301f0415 0xec100780 0xd0050409 0x04008780
0x0c002001 0xe4208780 0x1000d015 0x0423c784
0x1c00f009 0x0423c780 0xd005c015 0x04204784
0xdc02e009 0x04204780 0xd0050409 0x04004780
0x0c002001 0xe4208780 0x1c00e409 0x0423c780
0x1c00ec15 0x0423c780 0xdc02e009 0x04204780
0xdc05e815 0x04204780 0xd0050409 0x04004780
0x307c07fd 0x6c0087e8 0x0c002001 0xe4208780
0xa0000609 0x44066500 0x30170409 0xec102500
0x31000415 0x0443e500 0x10001c15 0x2440e280
0x1400f609 0x0423c780 0xd402f409 0x042047e0
0xa0000409 0x44066680 0x30170409 0xec102680
0x31000409 0x0443e680 0x10001c09 0x2440e100
0x3088e3fd 0x6c60c7e8 0x308ae21d 0x6c60c780
0x100d8019 0x00000003 0xd0850e1d 0x04400780
0x10001219 0x2440e280 0x308be221 0x6c60c780
0x20400c19 0x0401c780 0xd085101d 0x04400780
0x3000ec15 0x04214780 0x20400c19 0x0401c780
0x20018a15 0x00000003 0x3006ec19 0xac200780
0x30060a15 0xac000780 0x308c0a15 0x8c400780
0x20000409 0x04014780 0x202e8409 0x0fffffff
0x30810419 0xac400780 0x10018009 0x00000003
0x307c0c19 0x8c000780 0x3006041d 0xc4000780
0x203f8a21 0x0fffffff 0xa0000e15 0x44014780
0x30080409 0xc4000780 0xc0010a05 0x00000780
0x203f8415 0x0fffffff 0xa0000205 0x8c004780
0x30050205 0xac000780 0x30008409 0x00000003
0x30020215 0x8c000780 0x301f0a05 0xec100780
0xd0010a05 0x04008780 0x0c002001 0xe4204780
0xdc010011 0x20000780 0x1000d009 0x0423c784
0x1c00f005 0x0423c780 0xd002c009 0x04204784
0xdc01e005 0x04204780 0xd0020205 0x04004780
0x0c002001 0xe4204780 0x1c00e405 0x0423c780
0x1c00ec09 0x0423c780 0xdc01e005 0x04204780
0xdc02e809 0x04204780 0xd0020205 0x04004780
0x0c002001 0xe4204780 0x1400f605 0x0423c780
0xd401f405 0x042047e0 0xa0000205 0x44066680
0x30170205 0xec102680 0x3100021d 0x0443e680
0x10001c1d 0x2440e100 0xa0187003 0x00000000
0x10187003 0x00000100 0x1100f008 0x1100ea04
0x41052c20 0x41032e24 0x30101021 0xc4100780
0x30101225 0xc4100780 0x60044c09 0x00220780
0x60024e05 0x00224780 0x20018404 0x20018004
0x30070209 0xc4100780 0x30060205 0xc4100780
0x20018404 0x2101e804 0x20088205 0x00000003
0xd00e0219 0xa0c00780 0x10187003 0x00000100
0x1100f008 0x1100ea04 0x41052c18 0x41032e20
0x30100c19 0xc4100780 0x30101021 0xc4100780
0x60044c09 0x00218780 0x60024e05 0x00220780
0x20018404 0x20018004 0x30070209 0xc4100780
0x30060205 0xc4100780 0x20018404 0x2101e808
0x30218e05 0x00000003 0x200c8409 0x00000003
0xd00e0405 0xa0c00780 0x10187003 0x00000100
0x1100f008 0x1100ea04 0x41052c18 0x41032e1c
0x30100c19 0xc4100780 0x30100e1d 0xc4100780
0x60044c09 0x00218780 0x60024e05 0x0021c780
0x20018404 0x20018004 0x30070209 0xc4100780
0x30060205 0xc4100780 0x20000409 0x04004780
0x20018605 0x00000003 0x2000c809 0x04208780
0xd00e0405 0xa0c00780 0xf0000001 0xe0000002
0xa0198003 0x00000000 0x10198003 0x00001100
0x1100f008 0x1100ea04 0x41052c0c 0x41032e18
0x3010060d 0xc4100780 0x30100c19 0xc4100780
0x60044c09 0x0020c780 0x60024e05 0x00218780
0x20018404 0x20018004 0x30070209 0xc4100780
0x30060205 0xc4100780 0x20000405 0x04004780
0x30020809 0xc4100780 0x2101e804 0x20018404
0x20008205 0x00000007 0xd00e0215 0xa0c00780
0xd8004009 0x20000782 0x20088001 0x00000003
0x3000d1fd 0x6c2107d8 0x100fb003 0x00001280
0xf0000001 0xe0000001
0x10000005 0x0403c780 0xa0000401 0x04000780
0x308001fd 0x644107c8 0xa0011003 0x00000000
0x10011003 0x00000280 0x1000ca09 0x0423c780
0x40054e0d 0x00200780 0x3010060d 0xc4100780
0x60044e09 0x0020c780 0x3007040d 0xc4100780
0x30060409 0xc4100780 0x2000060d 0x04008780
0x30020009 0xc4100780 0x2103e80c 0x20038408
0xd00e0409 0x80c00780 0x00020005 0xc0000780
0x04001601 0xe4208780 0x307c01fd 0x6c0147ca
0xa0027003 0x00000000 0x10027003 0x00000280
0x1000d209 0x0423c780 0x40054e0d 0x00200780
0x3010060d 0xc4100780 0x60044e09 0x0020c780
0xa0004c0d 0x04200780 0x2000040d 0x0400c780
0x2101ee09 0x00000003 0x40070811 0x00000780
0x60060a11 0x00010780 0x30100811 0xc4100780
0x6006080d 0x00010780 0x1000d409 0x0423c780
0x40070811 0x00000780 0x60060a15 0x00010780
0x30050611 0xc4100780 0x30100a15 0xc4100780
0x00017801 0xe4210780 0x60060809 0x00014780
0x00017a01 0xe4208780 0x307ccffd 0x6c2047ca
0x10054003 0x00000280 0x200b8009 0x00000003
0x00020405 0xc0000780 0x307c01fd 0x640087c8
0x3000d5fd 0x6c2107d8 0x1000f811 0x0403c780
0x2101ee0d 0x00000003 0xa0043003 0x00000000
0x04002001 0xe43f0780 0x10000009 0x0403c780
0x10043003 0x00001100 0x40010c15 0x00000780
0x60000e15 0x00014780 0x30100a19 0xc4100780
0x30050615 0xc4100780 0x60000c19 0x00018780
0xd005e809 0x20000780 0x2906e01c 0x2007881c
0x30020e1d 0xc4100780 0x2000cc1d 0x0421c780
0xd00e0e1d 0x80c00780 0x20208409 0x00000003
0xb400e01d 0x0021c780 0x3002d5fd 0x6c2107e8
0x04002001 0xe421c780 0x20000c19 0x04014780
0x10038003 0x00002280 0x1400f009 0x0423c782
0xd4010009 0x20000780 0xb400e009 0x00208780
0xb9026008 0xb9027008 0x04002001 0xe4208780
0x1400e409 0x0423c780 0xb400e009 0x00208780
0xb400e809 0x00208780 0xb400ec09 0x00208780
0x04002001 0xe4208780 0x1400e209 0x0423c680
0x00020809 0xc0000680 0xb400e009 0x00208680
0x08013601 0xe4208680 0x20018811 0x00000003
0x300407fd 0x6c0147e8 0x1002f003 0x00002280
0xd0820609 0x00400780 0xa00005fd 0x040007c8
0x30000003 0x00000280 0x00020005 0xc0000780
0xd404e009 0x20000780 0x1900e008 0x1900e004
0x0400b601 0xe4208780 0xd004d809 0x20000780
0x04007601 0xe43f0780 0x307ccffd 0x6c20c7c8
0x1800c009 0x0423c780 0x10090003 0x00000280
0x307c01fd 0x640087c8 0x213fee11 0x0fffffff
0x1000f815 0x0403c780 0xd002d809 0x20000780
0xb08205fd 0x605107d8 0x1000040d 0x0403c780
0xa800c019 0xe4204780 0xc0830c19 0x00401680
0xc083060d 0x00401680 0x9000060c 0x2045881c
0xc0030c19 0x00000780 0xd002d809 0x20000780
0x300701fd 0x640187d8 0xe806c009 0x00208780
0xd402e009 0x20001500 0x1800c00d 0x0423d500
0xe0010c0d 0x0000d500 0xe806c005 0x00205500
0x0400b601 0xe420d500 0x00020a09 0xc0000680
0x0800f601 0xe4208680 0x20400a0d 0x04000780
0x00020609 0xc0000780 0xd801d00d 0x20000780
0x30000bfd 0x6400c7d8 0xcc06c01d 0x00200780
0x1000f81d 0x0403d280 0x30000bfd 0x640147d8
0xb0000e0d 0x00018780 0x10000e0d 0x0403d280
0xd401d80d 0x20000780 0xbc00c00d 0x0020c780
0x30000bfd 0x640047d8 0x04007601 0xe420c780
0xa008c003 0x00000000 0x1008c003 0x00001280
0xd005e00d 0x20000780 0x30050a0d 0xc4100780
0x2d03e00c 0x2003800c 0xd801d809 0x20000780
0x30020619 0xc4100780 0xa800c00d 0xe4204780
0x2000d019 0x04218780 0xd00e0c0d 0xa0c00780
0xf0000001 0xe0000002 0x20018a15 0x00000003
0x3005cffd 0x6c2147d8 0x10063003 0x00001280
0x3000cffd 0x6420c7c8 0x30000003 0x00000280
0xd005e009 0x20000780 0x3005ce05 0xc4300780
0x2901e004 0x20018000 0xd403d805 0x20000780
0x30020005 0xc4100780 0x1500e000 0x2101f004
0xd00e0201 0xa0c00781
}
}
code {