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opencl flac encoder

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chudov
2010-10-10 23:28:38 +00:00
parent facb0338c5
commit 04ca40e627
2 changed files with 364 additions and 314 deletions
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511
CUETools.Codecs.FLACCL/flac.cl
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@@ -20,6 +20,8 @@
#ifndef _FLACCL_KERNEL_H_
#define _FLACCL_KERNEL_H_
//#pragma OPENCL EXTENSION cl_amd_fp64 : enable
typedef enum
{
Constant = 0,
@@ -116,7 +118,7 @@ void cudaFindWastedBits(
barrier(CLK_LOCAL_MEM_FENCE);
int w = 0, a = 0;
for (int pos = 0; pos < task.blocksize; pos += get_local_size(0))
for (int pos = 0; pos < task.blocksize; pos += GROUP_SIZE)
{
int smp = pos + tid < task.blocksize ? samples[task.samplesOffs + pos + tid] : 0;
w |= smp;
@@ -126,7 +128,7 @@ void cudaFindWastedBits(
abits[tid] = a;
barrier(CLK_LOCAL_MEM_FENCE);
for (int s = get_local_size(0) / 2; s > 0; s >>= 1)
for (int s = GROUP_SIZE / 2; s > 0; s >>= 1)
{
if (tid < s)
{
@@ -200,6 +202,12 @@ void cudaComputeAutocor(
output[get_group_id(1) * (MAX_ORDER + 1) + tid + lag0] = product[tid * (GROUP_SIZE >> 2)];
}
//#define DEBUGPRINT
#ifdef DEBUGPRINT
#pragma OPENCL EXTENSION cl_amd_printf : enable
#endif
__kernel __attribute__((reqd_work_group_size(32, 1, 1)))
void cudaComputeLPC(
__global FLACCLSubframeTask *tasks,
@@ -241,12 +249,20 @@ void cudaComputeLPC(
float gen0 = shared.gen1[get_local_id(0)] = shared.autoc[get_local_id(0)+1];
shared.ldr[get_local_id(0)] = 0.0f;
float error = shared.autoc[0];
#ifdef DEBUGPRINT
int magic = shared.autoc[0] == 177286873088.0f;
if (magic && get_local_id(0) <= MAX_ORDER)
printf("autoc[%d] == %f\n", get_local_id(0), shared.autoc[get_local_id(0)]);
#endif
barrier(CLK_LOCAL_MEM_FENCE);
for (int order = 0; order < MAX_ORDER; order++)
{
// Schur recursion
float reff = -shared.gen1[0] / error;
error += shared.gen1[0] * reff; // Equivalent to error *= (1 - reff * reff);
//error *= (1 - reff * reff);
float gen1;
if (get_local_id(0) < MAX_ORDER - 1 - order)
{
@@ -256,6 +272,12 @@ void cudaComputeLPC(
barrier(CLK_LOCAL_MEM_FENCE);
if (get_local_id(0) < MAX_ORDER - 1 - order)
shared.gen1[get_local_id(0)] = gen1;
#ifdef DEBUGPRINT
if (magic && get_local_id(0) == 0)
printf("order == %d, reff == %f, error = %f\n", order, reff, error);
if (magic && get_local_id(0) <= MAX_ORDER)
printf("gen[%d] == %f, %f\n", get_local_id(0), gen0, gen1);
#endif
// Store prediction error
if (get_local_id(0) == 0)
@@ -272,6 +294,8 @@ void cudaComputeLPC(
// Output coeffs
if (get_local_id(0) <= order)
lpcs[shared.lpcOffs + order * 32 + get_local_id(0)] = -shared.ldr[order - get_local_id(0)];
//if (get_local_id(0) <= order + 1 && fabs(-shared.ldr[0]) > 3000)
// printf("coef[%d] == %f, autoc == %f, error == %f\n", get_local_id(0), -shared.ldr[order - get_local_id(0)], shared.autoc[get_local_id(0)], shared.error[get_local_id(0)]);
}
barrier(CLK_LOCAL_MEM_FENCE);
// Output prediction error estimates
@@ -309,12 +333,12 @@ void cudaQuantizeLPC(
// Select best orders based on Akaike's Criteria
shared.index[tid] = min(MAX_ORDER - 1, tid);
shared.error[tid] = shared.task.blocksize * 64 + tid;
shared.index[32 + tid] = min(MAX_ORDER - 1, tid);
shared.error[32 + tid] = shared.task.blocksize * 64 + tid;
shared.index[32 + tid] = MAX_ORDER - 1;
shared.error[32 + tid] = shared.task.blocksize * 64 + tid + 32;
// Load prediction error estimates
if (tid < MAX_ORDER)
shared.error[tid] = shared.task.blocksize * log(lpcs[shared.lpcOffs + MAX_ORDER * 32 + tid]) + tid * 5.12f * log(shared.task.blocksize);
shared.error[tid] = shared.task.blocksize * log(lpcs[shared.lpcOffs + MAX_ORDER * 32 + tid]) + tid * 4.12f * log(shared.task.blocksize);
//shared.error[get_local_id(0)] = shared.task.blocksize * log(lpcs[shared.lpcOffs + MAX_ORDER * 32 + get_local_id(0)]) + get_local_id(0) * 0.30f * (shared.task.abits + 1) * log(shared.task.blocksize);
barrier(CLK_LOCAL_MEM_FENCE);
@@ -361,6 +385,9 @@ void cudaQuantizeLPC(
}
}
//shared.index[tid] = MAX_ORDER - 1;
//barrier(CLK_LOCAL_MEM_FENCE);
// Quantization
for (int i = 0; i < taskCountLPC; i ++)
{
@@ -410,21 +437,20 @@ void cudaQuantizeLPC(
cbits = 1 + 32 - clz(shared.tmpi[0] | shared.tmpi[1]);
// output shift, cbits and output coeffs
if (i < taskCountLPC)
{
int taskNo = get_group_id(1) * taskCount + get_group_id(0) * taskCountLPC + i;
if (tid == 0)
tasks[taskNo].data.shift = shift;
if (tid == 0)
tasks[taskNo].data.cbits = cbits;
if (tid == 0)
tasks[taskNo].data.residualOrder = order + 1;
if (tid <= order)
tasks[taskNo].coefs[tid] = coef;
}
int taskNo = get_group_id(1) * taskCount + get_group_id(0) * taskCountLPC + i;
if (tid == 0)
tasks[taskNo].data.shift = shift;
if (tid == 0)
tasks[taskNo].data.cbits = cbits;
if (tid == 0)
tasks[taskNo].data.residualOrder = order + 1;
if (tid <= order)
tasks[taskNo].coefs[tid] = coef;
}
}
#define DONT_BEACCURATE
__kernel /*__attribute__(( vec_type_hint (int4)))*/ __attribute__((reqd_work_group_size(GROUP_SIZE, 1, 1)))
void cudaEstimateResidual(
__global int*output,
@@ -432,10 +458,14 @@ void cudaEstimateResidual(
__global FLACCLSubframeTask *tasks
)
{
__local float data[GROUP_SIZE * 2];
__local int residual[GROUP_SIZE];
__local int data[GROUP_SIZE * 2];
__local FLACCLSubframeTask task;
__local float4 coefsf4[8];
#ifdef BEACCURATE
__local int residual[GROUP_SIZE];
__local int len[GROUP_SIZE / 16];
#else
__local float residual[GROUP_SIZE];
#endif
const int tid = get_local_id(0);
if (tid < sizeof(task)/sizeof(int))
@@ -444,56 +474,79 @@ void cudaEstimateResidual(
int ro = task.data.residualOrder;
int bs = task.data.blocksize;
float res = 0;
if (tid < 32)
((__local float *)&coefsf4[0])[tid] = select(0.0f, ((float)task.coefs[tid]) / (1 << task.data.shift), tid < ro);
data[tid] = tid < bs ? (float)(samples[task.data.samplesOffs + tid] >> task.data.wbits) : 0.0f;
if (tid < 32 && tid >= ro)
task.coefs[tid] = 0;
#ifdef BEACCURATE
if (tid < GROUP_SIZE / 16)
len[tid] = 0;
#else
float res = 0.0f;
#endif
data[tid] = tid < bs ? samples[task.data.samplesOffs + tid] >> task.data.wbits : 0;
for (int pos = 0; pos < bs; pos += GROUP_SIZE)
{
// fetch samples
float nextData = pos + tid + GROUP_SIZE < bs ? (float)(samples[task.data.samplesOffs + pos + tid + GROUP_SIZE] >> task.data.wbits) : 0.0f;
int nextData = pos + tid + GROUP_SIZE < bs ? samples[task.data.samplesOffs + pos + tid + GROUP_SIZE] >> task.data.wbits : 0;
data[tid + GROUP_SIZE] = nextData;
barrier(CLK_LOCAL_MEM_FENCE);
// compute residual
__local float4 * dptr = (__local float4 *)&data[tid];
float sumf = data[tid + ro] -
( dot(dptr[0], coefsf4[0])
+ dot(dptr[1], coefsf4[1])
__local int4 * dptr = (__local int4 *)&data[tid];
__local int4 * cptr = (__local int4 *)&task.coefs[0];
int4 sum = dptr[0] * cptr[0]
#if MAX_ORDER > 4
+ dptr[1] * cptr[1]
#if MAX_ORDER > 8
+ dot(dptr[2], coefsf4[2])
+ dptr[2] * cptr[2]
#if MAX_ORDER > 12
+ dot(dptr[3], coefsf4[3])
+ dptr[3] * cptr[3]
#if MAX_ORDER > 16
+ dot(dptr[4], coefsf4[4])
+ dot(dptr[5], coefsf4[5])
+ dot(dptr[6], coefsf4[6])
+ dot(dptr[7], coefsf4[7])
+ dptr[4] * cptr[4]
+ dptr[5] * cptr[5]
+ dptr[6] * cptr[6]
+ dptr[7] * cptr[7]
#endif
#endif
#endif
);
//residual[tid] = sum;
#endif
;
res += select(0.0f, min(fabs(sumf), (float)0x7fffff), pos + tid + ro < bs);
int t = select(0, data[tid + ro] - ((sum.x + sum.y + sum.z + sum.w) >> task.data.shift), pos + tid + ro < bs);
#ifdef BEACCURATE
residual[tid] = min((t << 1) ^ (t >> 31), 0x7fffff);
#else
res += fabs(t);
#endif
barrier(CLK_LOCAL_MEM_FENCE);
//int k = min(33 - clz(sum), 14);
//res += select(0, 1 + k, pos + tid + ro < bs);
//sum = residual[tid] + residual[tid + 1] + residual[tid + 2] + residual[tid + 3]
// + residual[tid + 4] + residual[tid + 5] + residual[tid + 6] + residual[tid + 7];
//int k = clamp(29 - clz(sum), 0, 14);
//res += select(0, 8 * (k + 1) + (sum >> k), pos + tid + ro < bs && !(tid & 7));
#ifdef BEACCURATE
if (tid < GROUP_SIZE / 16)
{
__local int4 * chunk = ((__local int4 *)residual) + tid * 4;
int4 sum = chunk[0] + chunk[1] + chunk[2] + chunk[3];
int res = sum.x + sum.y + sum.z + sum.w;
int k = clamp(clz(16) - clz(res), 0, 14);
len[tid] += 16 * k + (res >> k);
k = clamp(clz(16) - clz(res), 0, 14);
}
#endif
data[tid] = nextData;
}
int residualLen = (bs - ro) / GROUP_SIZE + select(0, 1, tid < (bs - ro) % GROUP_SIZE);
int k = clamp(convert_int_rtn(log2((res + 0.000001f) / (residualLen + 0.000001f))), 0, 14);
residual[tid] = residualLen * (k + 1) + (convert_int_rtz(res) >> k);
#ifdef BEACCURATE
barrier(CLK_LOCAL_MEM_FENCE);
for (int l = GROUP_SIZE / 32; l > 0; l >>= 1)
{
if (tid < l)
len[tid] += len[tid + l];
barrier(CLK_LOCAL_MEM_FENCE);
}
if (tid == 0)
output[get_group_id(0)] = len[0] + (bs - ro);
#else
residual[tid] = res;
barrier(CLK_LOCAL_MEM_FENCE);
for (int l = GROUP_SIZE / 2; l > 0; l >>= 1)
{
@@ -502,7 +555,16 @@ void cudaEstimateResidual(
barrier(CLK_LOCAL_MEM_FENCE);
}
if (tid == 0)
output[get_group_id(0)] = residual[0];
{
int residualLen = (bs - ro);
float sum = residual[0] * 2;// + residualLen / 2;
//int k = clamp(convert_int_rtn(log2((sum + 0.000001f) / (residualLen + 0.000001f))), 0, 14);
int k;
frexp((sum + 0.000001f) / residualLen, &k);
k = clamp(k - 1, 0, 14);
output[get_group_id(0)] = residualLen * (k + 1) + convert_int_rtn(min((float)0xffffff, sum / (1 << k)));
}
#endif
}
__kernel __attribute__((reqd_work_group_size(32, 1, 1)))
@@ -641,6 +703,7 @@ void cudaCopyBestMethodStereo(
tasks_out[2 * get_group_id(1) + 1].data.residualOffs = tasks[shared.best_index[1]].data.residualOffs;
}
// get_group_id(0) == task index
__kernel __attribute__((reqd_work_group_size(GROUP_SIZE, 1, 1)))
void cudaEncodeResidual(
__global int *output,
@@ -652,7 +715,7 @@ void cudaEncodeResidual(
__local int data[GROUP_SIZE * 2];
const int tid = get_local_id(0);
if (get_local_id(0) < sizeof(task) / sizeof(int))
((__local int*)&task)[get_local_id(0)] = ((__global int*)(&tasks[get_group_id(1)]))[get_local_id(0)];
((__local int*)&task)[get_local_id(0)] = ((__global int*)(&tasks[get_group_id(0)]))[get_local_id(0)];
barrier(CLK_LOCAL_MEM_FENCE);
int bs = task.data.blocksize;
@@ -679,6 +742,8 @@ void cudaEncodeResidual(
}
}
// get_group_id(0) == partition index
// get_group_id(1) == task index
__kernel __attribute__((reqd_work_group_size(GROUP_SIZE, 1, 1)))
void cudaCalcPartition(
__global int *partition_lengths,
@@ -697,8 +762,8 @@ void cudaCalcPartition(
((__local int*)&task)[tid] = ((__global int*)(&tasks[get_group_id(1)]))[tid];
barrier(CLK_LOCAL_MEM_FENCE);
int k = tid % (GROUP_SIZE / 16);
int x = tid / (GROUP_SIZE / 16);
int k = tid % 16;
int x = tid / 16;
int sum = 0;
for (int pos0 = 0; pos0 < psize; pos0 += GROUP_SIZE)
@@ -707,7 +772,7 @@ void cudaCalcPartition(
// fetch residual
int s = (offs >= task.residualOrder && pos0 + tid < psize) ? residual[task.residualOffs + offs] : 0;
// convert to unsigned
data[tid] = min(0xfffff, (s << 1) ^ (s >> 31));
data[tid] = min(0x7fffff, (s << 1) ^ (s >> 31));
barrier(CLK_LOCAL_MEM_FENCE);
// calc number of unary bits for each residual sample with each rice paramater
@@ -716,7 +781,7 @@ void cudaCalcPartition(
barrier(CLK_LOCAL_MEM_FENCE);
}
length[x][k] = min(0xfffff, sum);
length[x][k] = min(0x7fffff, sum);
barrier(CLK_LOCAL_MEM_FENCE);
if (x == 0)
@@ -726,174 +791,180 @@ void cudaCalcPartition(
// output length
const int pos = (15 << (max_porder + 1)) * get_group_id(1) + (k << (max_porder + 1));
if (k <= 14)
partition_lengths[pos + get_group_id(0)] = min(0xfffff,length[0][k]) + (psize - task.residualOrder * (get_group_id(0) == 0)) * (k + 1);
partition_lengths[pos + get_group_id(0)] = min(0x7fffff,length[0][k]) + (psize - task.residualOrder * (get_group_id(0) == 0)) * (k + 1);
}
}
//// Sums partition lengths for a certain k == get_group_id(0)
//// Requires 128 threads
//__kernel void cudaSumPartition(
// int* partition_lengths,
// int max_porder
// )
//{
// __local struct {
// volatile int data[512+32]; // max_porder <= 8, data length <= 1 << 9.
// } shared;
//
// const int pos = (15 << (max_porder + 1)) * get_group_id(1) + (get_group_id(0) << (max_porder + 1));
//
// // fetch partition lengths
// shared.data[get_local_id(0)] = get_local_id(0) < (1 << max_porder) ? partition_lengths[pos + get_local_id(0)] : 0;
// shared.data[get_local_size(0) + get_local_id(0)] = get_local_size(0) + get_local_id(0) < (1 << max_porder) ? partition_lengths[pos + get_local_size(0) + get_local_id(0)] : 0;
// barrier(CLK_LOCAL_MEM_FENCE);
//
// int in_pos = (get_local_id(0) << 1);
// int out_pos = (1 << max_porder) + get_local_id(0);
// int bs;
// for (bs = 1 << (max_porder - 1); bs > 32; bs >>= 1)
// {
// if (get_local_id(0) < bs) shared.data[out_pos] = shared.data[in_pos] + shared.data[in_pos + 1];
// in_pos += bs << 1;
// out_pos += bs;
// barrier(CLK_LOCAL_MEM_FENCE);
// }
// if (get_local_id(0) < 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;
// }
// barrier(CLK_LOCAL_MEM_FENCE);
// if (get_local_id(0) < (1 << max_porder))
// partition_lengths[pos + (1 << max_porder) + get_local_id(0)] = shared.data[(1 << max_porder) + get_local_id(0)];
// if (get_local_size(0) + get_local_id(0) < (1 << max_porder))
// partition_lengths[pos + (1 << max_porder) + get_local_size(0) + get_local_id(0)] = shared.data[(1 << max_porder) + get_local_size(0) + get_local_id(0)];
//}
//
//// Finds optimal rice parameter for up to 16 partitions at a time.
//// Requires 16x16 threads
//__kernel void cudaFindRiceParameter(
// int* rice_parameters,
// int* partition_lengths,
// int max_porder
// )
//{
// __local struct {
// volatile int length[256];
// volatile int index[256];
// } shared;
// const int tid = get_local_id(0) + (get_local_id(1) << 5);
// const int parts = min(32, 2 << max_porder);
// const int pos = (15 << (max_porder + 1)) * get_group_id(1) + (get_local_id(1) << (max_porder + 1));
//
// // read length for 32 partitions
// int l1 = (get_local_id(0) < parts) ? partition_lengths[pos + get_group_id(0) * 32 + get_local_id(0)] : 0xffffff;
// int l2 = (get_local_id(1) + 8 <= 14 && get_local_id(0) < parts) ? partition_lengths[pos + (8 << (max_porder + 1)) + get_group_id(0) * 32 + get_local_id(0)] : 0xffffff;
// // find best rice parameter
// shared.index[tid] = get_local_id(1) + ((l2 < l1) << 3);
// shared.length[tid] = l1 = min(l1, l2);
// barrier(CLK_LOCAL_MEM_FENCE);
// Sums partition lengths for a certain k == get_group_id(0)
// Requires 128 threads
// get_group_id(0) == k
// get_group_id(1) == task index
__kernel __attribute__((reqd_work_group_size(128, 1, 1)))
void cudaSumPartition(
__global int* partition_lengths,
int max_porder
)
{
__local int data[512]; // max_porder <= 8, data length <= 1 << 9.
const int pos = (15 << (max_porder + 1)) * get_group_id(1) + (get_group_id(0) << (max_porder + 1));
// fetch partition lengths
data[get_local_id(0)] = get_local_id(0) < (1 << max_porder) ? partition_lengths[pos + get_local_id(0)] : 0;
data[get_local_size(0) + get_local_id(0)] = get_local_size(0) + get_local_id(0) < (1 << max_porder) ? partition_lengths[pos + get_local_size(0) + get_local_id(0)] : 0;
barrier(CLK_LOCAL_MEM_FENCE);
int in_pos = (get_local_id(0) << 1);
int out_pos = (1 << max_porder) + get_local_id(0);
for (int bs = 1 << (max_porder - 1); bs > 0; bs >>= 1)
{
if (get_local_id(0) < bs) data[out_pos] = data[in_pos] + data[in_pos + 1];
in_pos += bs << 1;
out_pos += bs;
barrier(CLK_LOCAL_MEM_FENCE);
}
if (get_local_id(0) < (1 << max_porder))
partition_lengths[pos + (1 << max_porder) + get_local_id(0)] = data[(1 << max_porder) + get_local_id(0)];
if (get_local_size(0) + get_local_id(0) < (1 << max_porder))
partition_lengths[pos + (1 << max_porder) + get_local_size(0) + get_local_id(0)] = data[(1 << max_porder) + get_local_size(0) + get_local_id(0)];
}
// Finds optimal rice parameter for several partitions at a time.
// get_group_id(0) == chunk index (chunk size is GROUP_SIZE / 8, so total task size is 8 * (2 << max_porder))
// get_group_id(1) == task index
__kernel __attribute__((reqd_work_group_size(GROUP_SIZE, 1, 1)))
void cudaFindRiceParameter(
__global int* rice_parameters,
__global int* partition_lengths,
int max_porder
)
{
__local struct {
volatile int length[GROUP_SIZE];
volatile int index[GROUP_SIZE];
} shared;
const int tid = get_local_id(0);
const int ws = GROUP_SIZE / 8;
const int parts = min(ws, 2 << max_porder);
const int p = tid % ws;
const int k = tid / ws; // 0..7
const int pos = (15 << (max_porder + 1)) * get_group_id(1) + (k << (max_porder + 1));
// read length for 32 partitions
int l1 = (p < parts) ? partition_lengths[pos + get_group_id(0) * ws + p] : 0xffffff;
int l2 = (k + 8 <= 14 && p < parts) ? partition_lengths[pos + (8 << (max_porder + 1)) + get_group_id(0) * ws + p] : 0xffffff;
// find best rice parameter
shared.index[tid] = k + ((l2 < l1) << 3);
shared.length[tid] = l1 = min(l1, l2);
barrier(CLK_LOCAL_MEM_FENCE);
//#pragma unroll 3
// for (int sh = 7; sh >= 5; sh --)
// {
// if (tid < (1 << sh))
// {
// l2 = shared.length[tid + (1 << sh)];
// shared.index[tid] = shared.index[tid + ((l2 < l1) << sh)];
// shared.length[tid] = l1 = min(l1, l2);
// }
// barrier(CLK_LOCAL_MEM_FENCE);
// }
// if (tid < parts)
// {
// // output rice parameter
// rice_parameters[(get_group_id(1) << (max_porder + 2)) + get_group_id(0) * parts + tid] = shared.index[tid];
// // output length
// rice_parameters[(get_group_id(1) << (max_porder + 2)) + (1 << (max_porder + 1)) + get_group_id(0) * parts + tid] = shared.length[tid];
// }
//}
//
//__kernel void cudaFindPartitionOrder(
// int* best_rice_parameters,
// FLACCLSubframeTask *tasks,
// int* rice_parameters,
// int max_porder
// )
//{
// __local struct {
// int data[512];
// volatile int tmp[256];
// int length[32];
// int index[32];
// //char4 ch[64];
// FLACCLSubframeTask task;
// } shared;
// const int pos = (get_group_id(1) << (max_porder + 2)) + (2 << max_porder);
// if (get_local_id(0) < sizeof(shared.task) / sizeof(int))
// ((int*)&shared.task)[get_local_id(0)] = ((int*)(&tasks[get_group_id(1)]))[get_local_id(0)];
// // fetch partition lengths
// shared.data[get_local_id(0)] = get_local_id(0) < (2 << max_porder) ? rice_parameters[pos + get_local_id(0)] : 0;
// shared.data[get_local_id(0) + 256] = get_local_id(0) + 256 < (2 << max_porder) ? rice_parameters[pos + 256 + get_local_id(0)] : 0;
// barrier(CLK_LOCAL_MEM_FENCE);
//
// for (int porder = max_porder; porder >= 0; porder--)
// {
// shared.tmp[get_local_id(0)] = (get_local_id(0) < (1 << porder)) * shared.data[(2 << max_porder) - (2 << porder) + get_local_id(0)];
// barrier(CLK_LOCAL_MEM_FENCE);
// SUM256(shared.tmp, get_local_id(0), +=);
// if (get_local_id(0) == 0)
// shared.length[porder] = shared.tmp[0] + (4 << porder);
// barrier(CLK_LOCAL_MEM_FENCE);
// }
//
// if (get_local_id(0) < 32)
// {
// shared.index[get_local_id(0)] = get_local_id(0);
// if (get_local_id(0) > max_porder)
// shared.length[get_local_id(0)] = 0xfffffff;
// int l1 = shared.length[get_local_id(0)];
// #pragma unroll 4
// for (int sh = 3; sh >= 0; sh --)
// {
// int l2 = shared.length[get_local_id(0) + (1 << sh)];
// shared.index[get_local_id(0)] = shared.index[get_local_id(0) + ((l2 < l1) << sh)];
// shared.length[get_local_id(0)] = l1 = min(l1, l2);
// }
// if (get_local_id(0) == 0)
// tasks[get_group_id(1)].data.porder = shared.index[0];
// if (get_local_id(0) == 0)
// {
// int obits = shared.task.data.obits - shared.task.data.wbits;
// tasks[get_group_id(1)].data.size =
// shared.task.data.type == Fixed ? shared.task.data.residualOrder * obits + 6 + l1 :
// shared.task.data.type == LPC ? shared.task.data.residualOrder * obits + 6 + l1 + 4 + 5 + shared.task.data.residualOrder * shared.task.data.cbits :
// shared.task.data.type == Constant ? obits : obits * shared.task.data.blocksize;
// }
// }
// barrier(CLK_LOCAL_MEM_FENCE);
// int porder = shared.index[0];
// if (get_local_id(0) < (1 << porder))
// best_rice_parameters[(get_group_id(1) << max_porder) + get_local_id(0)] = rice_parameters[pos - (2 << porder) + get_local_id(0)];
// // FIXME: should be bytes?
// // if (get_local_id(0) < (1 << porder))
// //shared.tmp[get_local_id(0)] = rice_parameters[pos - (2 << porder) + get_local_id(0)];
// // barrier(CLK_LOCAL_MEM_FENCE);
// // if (get_local_id(0) < max(1, (1 << porder) >> 2))
// // {
// //char4 ch;
// //ch.x = shared.tmp[(get_local_id(0) << 2)];
// //ch.y = shared.tmp[(get_local_id(0) << 2) + 1];
// //ch.z = shared.tmp[(get_local_id(0) << 2) + 2];
// //ch.w = shared.tmp[(get_local_id(0) << 2) + 3];
// //shared.ch[get_local_id(0)] = ch
// // }
// // barrier(CLK_LOCAL_MEM_FENCE);
// // if (get_local_id(0) < max(1, (1 << porder) >> 2))
// //best_rice_parameters[(get_group_id(1) << max_porder) + get_local_id(0)] = shared.ch[get_local_id(0)];
//}
//
for (int lsh = GROUP_SIZE / 2; lsh >= ws; lsh >>= 1)
{
if (tid < lsh)
{
l2 = shared.length[tid + lsh];
shared.index[tid] = shared.index[tid + (l2 < l1) * lsh];
shared.length[tid] = l1 = min(l1, l2);
}
barrier(CLK_LOCAL_MEM_FENCE);
}
if (tid < parts)
{
// output rice parameter
rice_parameters[(get_group_id(1) << (max_porder + 2)) + get_group_id(0) * parts + tid] = shared.index[tid];
// output length
rice_parameters[(get_group_id(1) << (max_porder + 2)) + (1 << (max_porder + 1)) + get_group_id(0) * parts + tid] = shared.length[tid];
}
}
// get_group_id(0) == task index
__kernel __attribute__((reqd_work_group_size(GROUP_SIZE, 1, 1)))
void cudaFindPartitionOrder(
__global int* best_rice_parameters,
__global FLACCLSubframeTask *tasks,
__global int* rice_parameters,
int max_porder
)
{
__local struct {
int length[32];
int index[32];
} shared;
__local int partlen[GROUP_SIZE];
__local FLACCLSubframeData task;
const int pos = (get_group_id(0) << (max_porder + 2)) + (2 << max_porder);
if (get_local_id(0) < sizeof(task) / sizeof(int))
((__local int*)&task)[get_local_id(0)] = ((__global int*)(&tasks[get_group_id(0)]))[get_local_id(0)];
// fetch partition lengths
barrier(CLK_LOCAL_MEM_FENCE);
for (int porder = max_porder; porder >= 0; porder--)
{
int len = 0;
for (int offs = 0; offs < (1 << porder); offs += GROUP_SIZE)
len += offs + get_local_id(0) < (1 << porder) ? rice_parameters[pos + (2 << max_porder) - (2 << porder) + offs + get_local_id(0)] : 0;
partlen[get_local_id(0)] = len;
barrier(CLK_LOCAL_MEM_FENCE);
for (int l = min(GROUP_SIZE, 1 << porder) / 2; l > 0; l >>= 1)
{
if (get_local_id(0) < l)
partlen[get_local_id(0)] += partlen[get_local_id(0) + l];
barrier(CLK_LOCAL_MEM_FENCE);
}
if (get_local_id(0) == 0)
shared.length[porder] = partlen[0] + (4 << porder);
barrier(CLK_LOCAL_MEM_FENCE);
}
if (get_local_id(0) < 32 && get_local_id(0) > max_porder)
shared.length[get_local_id(0)] = 0xfffffff;
if (get_local_id(0) < 32)
shared.index[get_local_id(0)] = get_local_id(0);
barrier(CLK_LOCAL_MEM_FENCE);
int l1 = get_local_id(0) <= max_porder ? shared.length[get_local_id(0)] : 0xfffffff;
for (int sh = 3; sh >= 0; sh --)
{
if (get_local_id(0) < (1 << sh))
{
int l2 = shared.length[get_local_id(0) + (1 << sh)];
shared.index[get_local_id(0)] = shared.index[get_local_id(0) + ((l2 < l1) << sh)];
shared.length[get_local_id(0)] = l1 = min(l1, l2);
}
barrier(CLK_LOCAL_MEM_FENCE);
}
if (get_local_id(0) == 0)
tasks[get_group_id(0)].data.porder = shared.index[0];
if (get_local_id(0) == 0)
{
int obits = task.obits - task.wbits;
tasks[get_group_id(0)].data.size =
task.type == Fixed ? task.residualOrder * obits + 6 + l1 :
task.type == LPC ? task.residualOrder * obits + 6 + l1 + 4 + 5 + task.residualOrder * task.cbits :
task.type == Constant ? obits : obits * task.blocksize;
}
barrier(CLK_LOCAL_MEM_FENCE);
int porder = shared.index[0];
for (int offs = 0; offs < (1 << porder); offs += GROUP_SIZE)
if (offs + get_local_id(0) < (1 << porder))
best_rice_parameters[(get_group_id(0) << max_porder) + offs + get_local_id(0)] = rice_parameters[pos - (2 << porder) + offs + get_local_id(0)];
// FIXME: should be bytes?
// if (get_local_id(0) < (1 << porder))
//shared.tmp[get_local_id(0)] = rice_parameters[pos - (2 << porder) + get_local_id(0)];
// barrier(CLK_LOCAL_MEM_FENCE);
// if (get_local_id(0) < max(1, (1 << porder) >> 2))
// {
//char4 ch;
//ch.x = shared.tmp[(get_local_id(0) << 2)];
//ch.y = shared.tmp[(get_local_id(0) << 2) + 1];
//ch.z = shared.tmp[(get_local_id(0) << 2) + 2];
//ch.w = shared.tmp[(get_local_id(0) << 2) + 3];
//shared.ch[get_local_id(0)] = ch
// }
// barrier(CLK_LOCAL_MEM_FENCE);
// if (get_local_id(0) < max(1, (1 << porder) >> 2))
//best_rice_parameters[(get_group_id(1) << max_porder) + get_local_id(0)] = shared.ch[get_local_id(0)];
}
//#endif
//
//#if 0