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tidying up

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This commit is contained in:
chudov
2010-10-23 18:29:06 +00:00
parent 349123ec19
commit 3ccf418f6c
2 changed files with 207 additions and 213 deletions
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77
CUETools.Codecs.FLACCL/FLACCLWriter.cs
View File

@@ -1336,8 +1336,8 @@ namespace CUETools.Codecs.FLACCL
frame_count += nFrames;
frame_pos += nFrames * blocksize;
task.openCLCQ.EnqueueWriteBuffer(task.clSamplesBytes, false, 0, sizeof(short) * channels * blocksize * nFrames, task.clSamplesBytes.HostPtr);
//task.openCLCQ.EnqueueUnmapMemObject(task.cudaSamplesBytes, task.cudaSamplesBytes.HostPtr);
//task.openCLCQ.EnqueueMapBuffer(task.cudaSamplesBytes, true, MapFlags.WRITE, 0, task.samplesBufferLen / 2);
//task.openCLCQ.EnqueueUnmapMemObject(task.clSamplesBytes, task.clSamplesBytes.HostPtr);
//task.openCLCQ.EnqueueMapBuffer(task.clSamplesBytes, true, MapFlags.WRITE, 0, task.samplesBufferLen / 2);
}
unsafe void run_GPU_task(FLACCLTask task)
@@ -1467,6 +1467,9 @@ namespace CUETools.Codecs.FLACCL
OCLMan.Defines =
"#define MAX_ORDER " + eparams.max_prediction_order.ToString() + "\n" +
"#define GROUP_SIZE " + groupSize.ToString() + "\n" +
#if DEBUG
"#define DEBUG\n" +
#endif
_settings.Defines + "\n";
// The BuildOptions string is passed directly to clBuild and can be used to do debug builds etc
OCLMan.BuildOptions = "";
@@ -2230,8 +2233,7 @@ namespace CUETools.Codecs.FLACCL
int riceParamsLen = sizeof(int) * (4 << 8) * channels * FLACCLWriter.maxFrames;
int lpcDataLen = sizeof(float) * 32 * 33 * lpc.MAX_LPC_WINDOWS * channelsCount * FLACCLWriter.maxFrames;
clSamplesBytes = openCLProgram.Context.CreateBuffer(MemFlags.READ_WRITE | MemFlags.ALLOC_HOST_PTR, (uint)samplesBufferLen / 2);
//openCLCQ.EnqueueMapBuffer(cudaSamplesBytes, true, MapFlags.WRITE, 0, samplesBufferLen / 2);
clSamplesBytes = openCLProgram.Context.CreateBuffer(MemFlags.READ_ONLY | MemFlags.ALLOC_HOST_PTR, (uint)samplesBufferLen / 2);
clSamples = openCLProgram.Context.CreateBuffer(MemFlags.READ_WRITE, samplesBufferLen);
clResidual = openCLProgram.Context.CreateBuffer(MemFlags.READ_WRITE | MemFlags.ALLOC_HOST_PTR, samplesBufferLen);
clLPCData = openCLProgram.Context.CreateBuffer(MemFlags.READ_WRITE, lpcDataLen);
@@ -2244,24 +2246,26 @@ namespace CUETools.Codecs.FLACCL
clResidualOutput = openCLProgram.Context.CreateBuffer(MemFlags.READ_WRITE, sizeof(int) * channelsCount * (lpc.MAX_LPC_WINDOWS * lpc.MAX_LPC_ORDER + 8) * 64 /*FLACCLWriter.maxResidualParts*/ * FLACCLWriter.maxFrames);
clWindowFunctions = openCLProgram.Context.CreateBuffer(MemFlags.READ_ONLY | MemFlags.ALLOC_HOST_PTR, sizeof(float) * FLACCLWriter.MAX_BLOCKSIZE /** 2*/ * lpc.MAX_LPC_WINDOWS);
clComputeAutocor = openCLProgram.CreateKernel("cudaComputeAutocor");
clStereoDecorr = openCLProgram.CreateKernel("cudaStereoDecorr");
//cudaChannelDecorr = openCLProgram.CreateKernel("cudaChannelDecorr");
clChannelDecorr2 = openCLProgram.CreateKernel("cudaChannelDecorr2");
clFindWastedBits = openCLProgram.CreateKernel("cudaFindWastedBits");
clComputeLPC = openCLProgram.CreateKernel("cudaComputeLPC");
clQuantizeLPC = openCLProgram.CreateKernel("cudaQuantizeLPC");
//cudaComputeLPCLattice = openCLProgram.CreateKernel("cudaComputeLPCLattice");
clEstimateResidual = openCLProgram.CreateKernel("cudaEstimateResidual");
clChooseBestMethod = openCLProgram.CreateKernel("cudaChooseBestMethod");
clCopyBestMethod = openCLProgram.CreateKernel("cudaCopyBestMethod");
clCopyBestMethodStereo = openCLProgram.CreateKernel("cudaCopyBestMethodStereo");
clEncodeResidual = openCLProgram.CreateKernel("cudaEncodeResidual");
clCalcPartition = openCLProgram.CreateKernel("cudaCalcPartition");
clCalcPartition16 = openCLProgram.CreateKernel("cudaCalcPartition16");
clSumPartition = openCLProgram.CreateKernel("cudaSumPartition");
clFindRiceParameter = openCLProgram.CreateKernel("cudaFindRiceParameter");
clFindPartitionOrder = openCLProgram.CreateKernel("cudaFindPartitionOrder");
//openCLCQ.EnqueueMapBuffer(clSamplesBytes, true, MapFlags.WRITE, 0, samplesBufferLen / 2);
clComputeAutocor = openCLProgram.CreateKernel("clComputeAutocor");
clStereoDecorr = openCLProgram.CreateKernel("clStereoDecorr");
//cudaChannelDecorr = openCLProgram.CreateKernel("clChannelDecorr");
clChannelDecorr2 = openCLProgram.CreateKernel("clChannelDecorr2");
clFindWastedBits = openCLProgram.CreateKernel("clFindWastedBits");
clComputeLPC = openCLProgram.CreateKernel("clComputeLPC");
clQuantizeLPC = openCLProgram.CreateKernel("clQuantizeLPC");
//cudaComputeLPCLattice = openCLProgram.CreateKernel("clComputeLPCLattice");
clEstimateResidual = openCLProgram.CreateKernel("clEstimateResidual");
clChooseBestMethod = openCLProgram.CreateKernel("clChooseBestMethod");
clCopyBestMethod = openCLProgram.CreateKernel("clCopyBestMethod");
clCopyBestMethodStereo = openCLProgram.CreateKernel("clCopyBestMethodStereo");
clEncodeResidual = openCLProgram.CreateKernel("clEncodeResidual");
clCalcPartition = openCLProgram.CreateKernel("clCalcPartition");
clCalcPartition16 = openCLProgram.CreateKernel("clCalcPartition16");
clSumPartition = openCLProgram.CreateKernel("clSumPartition");
clFindRiceParameter = openCLProgram.CreateKernel("clFindRiceParameter");
clFindPartitionOrder = openCLProgram.CreateKernel("clFindPartitionOrder");
samplesBuffer = new int[FLACCLWriter.MAX_BLOCKSIZE * channelsCount];
outputBuffer = new byte[max_frame_size * FLACCLWriter.maxFrames + 1];
@@ -2377,14 +2381,13 @@ namespace CUETools.Codecs.FLACCL
clSamples,
clWindowFunctions,
clResidualTasks,
nWindowFunctions - 1,
nResidualTasksPerChannel);
openCLCQ.EnqueueNDRangeKernel(
clComputeAutocor,
groupSize, 1,
eparams.max_prediction_order / 4 + 1,
nWindowFunctions * channelsCount * frameCount);
channelsCount * frameCount,
nWindowFunctions);
clComputeLPC.SetArgs(
clResidualTasks,
@@ -2491,7 +2494,7 @@ namespace CUETools.Codecs.FLACCL
openCLCQ.EnqueueNDRangeKernel(
clCalcPartition,
groupSize, 1,
1 << max_porder,
1 + ((1 << max_porder) - 1) / (groupSize / 16),
channels * frameCount);
}
@@ -2516,7 +2519,7 @@ namespace CUETools.Codecs.FLACCL
openCLCQ.EnqueueNDRangeKernel(
clFindRiceParameter,
groupSize, 1,
Math.Max(1, 8 * (2 << max_porder) / groupSize),
Math.Max(1, (2 << max_porder) / groupSize),
channels * frameCount);
//if (max_porder > 0) // need to run even if max_porder==0 just to calculate the final frame size
@@ -2531,18 +2534,18 @@ namespace CUETools.Codecs.FLACCL
groupSize,
channels * frameCount);
//openCLCQ.EnqueueReadBuffer(cudaBestRiceParams, false, 0, sizeof(int) * (1 << max_porder) * channels * frameCount, cudaBestRiceParams.HostPtr);
//openCLCQ.EnqueueReadBuffer(cudaResidual, false, 0, sizeof(int) * MAX_BLOCKSIZE * channels, cudaResidual.HostPtr);
openCLCQ.EnqueueMapBuffer(clBestRiceParams, false, MapFlags.READ, 0, sizeof(int) * (1 << max_porder) * channels * frameCount);
openCLCQ.EnqueueUnmapMemObject(clBestRiceParams, clBestRiceParams.HostPtr);
openCLCQ.EnqueueMapBuffer(clResidual, false, MapFlags.READ, 0, sizeof(int) * FLACCLWriter.MAX_BLOCKSIZE * channels);
openCLCQ.EnqueueUnmapMemObject(clResidual, clResidual.HostPtr);
openCLCQ.EnqueueReadBuffer(clBestRiceParams, false, 0, sizeof(int) * (1 << max_porder) * channels * frameCount, clBestRiceParams.HostPtr);
openCLCQ.EnqueueReadBuffer(clResidual, false, 0, sizeof(int) * FLACCLWriter.MAX_BLOCKSIZE * channels, clResidual.HostPtr);
//openCLCQ.EnqueueMapBuffer(clBestRiceParams, false, MapFlags.READ, 0, sizeof(int) * (1 << max_porder) * channels * frameCount);
//openCLCQ.EnqueueUnmapMemObject(clBestRiceParams, clBestRiceParams.HostPtr);
//openCLCQ.EnqueueMapBuffer(clResidual, false, MapFlags.READ, 0, sizeof(int) * FLACCLWriter.MAX_BLOCKSIZE * channels);
//openCLCQ.EnqueueUnmapMemObject(clResidual, clResidual.HostPtr);
}
//openCLCQ.EnqueueReadBuffer(cudaBestResidualTasks, false, 0, sizeof(FLACCLSubframeTask) * channels * frameCount, cudaBestResidualTasks.HostPtr);
openCLCQ.EnqueueMapBuffer(clBestResidualTasks, false, MapFlags.READ, 0, sizeof(FLACCLSubframeTask) * channels * frameCount);
openCLCQ.EnqueueUnmapMemObject(clBestResidualTasks, clBestResidualTasks.HostPtr);
openCLCQ.EnqueueReadBuffer(clBestResidualTasks, false, 0, sizeof(FLACCLSubframeTask) * channels * frameCount, clBestResidualTasks.HostPtr);
//openCLCQ.EnqueueMapBuffer(clBestResidualTasks, false, MapFlags.READ, 0, sizeof(FLACCLSubframeTask) * channels * frameCount);
//openCLCQ.EnqueueUnmapMemObject(clBestResidualTasks, clBestResidualTasks.HostPtr);
//openCLCQ.EnqueueMapBuffer(cudaSamplesBytes, false, MapFlags.WRITE, 0, samplesBufferLen / 2);
//openCLCQ.EnqueueMapBuffer(clSamplesBytes, false, MapFlags.WRITE, 0, samplesBufferLen / 2);
}
}
}

343
CUETools.Codecs.FLACCL/flac.cl
View File

@@ -20,6 +20,12 @@
#ifndef _FLACCL_KERNEL_H_
#define _FLACCL_KERNEL_H_
#ifdef DEBUG
#pragma OPENCL EXTENSION cl_amd_printf : enable
#endif
#pragma OPENCL EXTENSION cl_khr_local_int32_base_atomics : enable
//#pragma OPENCL EXTENSION cl_amd_fp64 : enable
typedef enum
@@ -55,7 +61,7 @@ typedef struct
int coefs[32]; // fixme: should be short?
} FLACCLSubframeTask;
__kernel void cudaStereoDecorr(
__kernel void clStereoDecorr(
__global int *samples,
__global short2 *src,
int offset
@@ -72,7 +78,7 @@ __kernel void cudaStereoDecorr(
}
}
__kernel void cudaChannelDecorr2(
__kernel void clChannelDecorr2(
__global int *samples,
__global short2 *src,
int offset
@@ -87,7 +93,7 @@ __kernel void cudaChannelDecorr2(
}
}
//__kernel void cudaChannelDecorr(
//__kernel void clChannelDecorr(
// int *samples,
// short *src,
// int offset
@@ -102,7 +108,7 @@ __kernel void cudaChannelDecorr2(
//#define __ffs(a) (33 - clz(~a & (a - 1)))
__kernel __attribute__((reqd_work_group_size(GROUP_SIZE, 1, 1)))
void cudaFindWastedBits(
void clFindWastedBits(
__global FLACCLSubframeTask *tasks,
__global int *samples,
int tasksPerChannel
@@ -115,12 +121,13 @@ void cudaFindWastedBits(
int tid = get_local_id(0);
if (tid < sizeof(task) / sizeof(int))
((__local int*)&task)[tid] = ((__global int*)(&tasks[get_group_id(0) * tasksPerChannel].data))[tid];
barrier(CLK_LOCAL_MEM_FENCE);
barrier(CLK_LOCAL_MEM_FENCE);
int w = 0, a = 0;
for (int pos = 0; pos < task.blocksize; pos += GROUP_SIZE)
for (int pos = tid; pos + tid < task.blocksize; pos += GROUP_SIZE)
{
int smp = pos + tid < task.blocksize ? samples[task.samplesOffs + pos + tid] : 0;
int smp = samples[task.samplesOffs + pos];
w |= smp;
a |= smp ^ (smp >> 31);
}
@@ -146,37 +153,39 @@ void cudaFindWastedBits(
tasks[get_group_id(0) * tasksPerChannel + tid].data.abits = a;
}
// get_num_groups(0) == number of tasks
// get_num_groups(1) == number of windows
__kernel __attribute__((reqd_work_group_size(GROUP_SIZE, 1, 1)))
void cudaComputeAutocor(
void clComputeAutocor(
__global float *output,
__global const int *samples,
__global const float *window,
__global FLACCLSubframeTask *tasks,
const int windowCount, // windows (log2: 0,1)
const int taskCount // tasks per block
)
{
__local float data[GROUP_SIZE * 2];
__local float product[GROUP_SIZE];
__local float product[(MAX_ORDER / 4 + 1) * GROUP_SIZE];
__local FLACCLSubframeData task;
const int tid = get_local_id(0);
// fetch task data
if (tid < sizeof(task) / sizeof(int))
((__local int*)&task)[tid] = ((__global int*)(tasks + taskCount * (get_group_id(1) >> windowCount)))[tid];
((__local int*)&task)[tid] = ((__global int*)(tasks + taskCount * get_group_id(0)))[tid];
for (int ord4 = 0; ord4 < (MAX_ORDER / 4 + 1); ord4 ++)
product[ord4 * GROUP_SIZE + tid] = 0.0f;
barrier(CLK_LOCAL_MEM_FENCE);
int bs = task.blocksize;
int windowOffs = (get_group_id(1) & ((1 << windowCount)-1)) * bs;
int windowOffs = get_group_id(1) * bs;
data[tid] = tid < bs ? samples[task.samplesOffs + tid] * window[windowOffs + tid] : 0.0f;
int tid0 = tid % (GROUP_SIZE >> 2);
int tid1 = tid / (GROUP_SIZE >> 2);
int lag0 = get_group_id(0) * 4;
__local float4 * dptr = ((__local float4 *)&data[0]) + tid0;
__local float4 * dptr1 = ((__local float4 *)&data[lag0 + tid1]) + tid0;
__local float4 * dptr1 = ((__local float4 *)&data[tid1]) + tid0;
float prod = 0.0f;
for (int pos = 0; pos < bs; pos += GROUP_SIZE)
{
// fetch samples
@@ -184,32 +193,26 @@ void cudaComputeAutocor(
data[tid + GROUP_SIZE] = nextData;
barrier(CLK_LOCAL_MEM_FENCE);
prod += dot(*dptr, *dptr1);
for (int ord4 = 0; ord4 < (MAX_ORDER / 4 + 1); ord4 ++)
product[ord4 * GROUP_SIZE + tid] += dot(dptr[0], dptr1[ord4]);
barrier(CLK_LOCAL_MEM_FENCE);
data[tid] = nextData;
}
product[tid] = prod;
barrier(CLK_LOCAL_MEM_FENCE);
for (int l = (GROUP_SIZE >> 3); l > 0; l >>= 1)
{
if (tid0 < l)
product[tid] = product[tid] + product[tid + l];
barrier(CLK_LOCAL_MEM_FENCE);
}
if (tid < 4 && tid + lag0 <= MAX_ORDER)
output[get_group_id(1) * (MAX_ORDER + 1) + tid + lag0] = product[tid * (GROUP_SIZE >> 2)];
for (int ord4 = 0; ord4 < (MAX_ORDER / 4 + 1); ord4 ++)
for (int l = (GROUP_SIZE >> 3); l > 0; l >>= 1)
{
if (tid0 < l)
product[ord4 * GROUP_SIZE + tid] += product[ord4 * GROUP_SIZE + tid + l];
barrier(CLK_LOCAL_MEM_FENCE);
}
if (tid <= MAX_ORDER)
output[(get_group_id(0) * get_num_groups(1) + get_group_id(1)) * (MAX_ORDER + 1) + tid] = 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(
void clComputeLPC(
__global FLACCLSubframeTask *tasks,
__global float *autoc,
__global float *lpcs,
@@ -250,7 +253,7 @@ void cudaComputeLPC(
shared.ldr[get_local_id(0)] = 0.0f;
float error = shared.autoc[0];
#ifdef DEBUGPRINT
#ifdef DEBUGPRINT1
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)]);
@@ -261,8 +264,8 @@ void cudaComputeLPC(
{
// Schur recursion
float reff = -shared.gen1[0] / error;
error += shared.gen1[0] * reff; // Equivalent to error *= (1 - reff * reff);
//error *= (1 - reff * reff);
//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)
{
@@ -272,7 +275,7 @@ 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
#ifdef DEBUGPRINT1
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)
@@ -304,7 +307,7 @@ void cudaComputeLPC(
}
__kernel __attribute__((reqd_work_group_size(32, 1, 1)))
void cudaQuantizeLPC(
void clQuantizeLPC(
__global FLACCLSubframeTask *tasks,
__global float*lpcs,
int taskCount, // tasks per block
@@ -449,8 +452,12 @@ void cudaQuantizeLPC(
}
}
#ifndef PARTORDER
#define PARTORDER 4
#endif
__kernel /*__attribute__(( vec_type_hint (int4)))*/ __attribute__((reqd_work_group_size(GROUP_SIZE, 1, 1)))
void cudaEstimateResidual(
void clEstimateResidual(
__global int*output,
__global int*samples,
__global FLACCLSubframeTask *tasks
@@ -459,7 +466,7 @@ void cudaEstimateResidual(
__local int data[GROUP_SIZE * 2];
__local FLACCLSubframeTask task;
__local int residual[GROUP_SIZE];
__local int len[GROUP_SIZE / 16];
__local int len[GROUP_SIZE >> PARTORDER];
const int tid = get_local_id(0);
if (tid < sizeof(task)/sizeof(int))
@@ -471,7 +478,7 @@ void cudaEstimateResidual(
if (tid < 32 && tid >= ro)
task.coefs[tid] = 0;
if (tid < GROUP_SIZE / 16)
if (tid < (GROUP_SIZE >> PARTORDER))
len[tid] = 0;
data[tid] = 0;
@@ -514,35 +521,49 @@ void cudaEstimateResidual(
#endif
;
int t = data[tid + GROUP_SIZE] - ((sum.x + sum.y + sum.z + sum.w) >> task.data.shift);
int t = nextData - ((sum.x + sum.y + sum.z + sum.w) >> task.data.shift);
// ensure we're within frame bounds
t = select(0, t, offs >= ro && offs < bs);
// overflow protection
t = clamp(t, -0x7fffff, 0x7fffff);
// convert to unsigned
residual[tid] = (t << 1) ^ (t >> 31);
barrier(CLK_GLOBAL_MEM_FENCE);
barrier(CLK_LOCAL_MEM_FENCE);
data[tid] = nextData;
// calculate rice partition bit length for every 16 samples
if (tid < GROUP_SIZE / 16)
if (tid < (GROUP_SIZE >> PARTORDER))
{
__local int4 * chunk = ((__local int4 *)residual) + (tid << 2);
int4 sum = chunk[0] + chunk[1] + chunk[2] + chunk[3];
int res = sum.x + sum.y + sum.z + sum.w;
int k = clamp(27 - clz(res), 0, 14); // 27 - clz(res) == clz(16) - clz(res) == log2(res / 16)
#ifdef EXTRAMODE
sum = (chunk[0] >> k) + (chunk[1] >> k) + (chunk[2] >> k) + (chunk[3] >> k);
len[tid] += (k << 4) + sum.x + sum.y + sum.z + sum.w;
//__local int4 * chunk = (__local int4 *)&residual[tid << PARTORDER];
__local int4 * chunk = ((__local int4 *)residual) + (tid << (PARTORDER - 2));
#if PARTORDER == 3
int4 sum = chunk[0] + chunk[1];
#elif PARTORDER == 4
int4 sum = chunk[0] + chunk[1] + chunk[2] + chunk[3]; // [0 .. (1 << (PARTORDER - 2)) - 1]
#elif PARTORDER == 5
int4 sum = chunk[0] + chunk[1] + chunk[2] + chunk[3] + chunk[4] + chunk[5] + chunk[6] + chunk[7];
#else
len[tid] += (k << 4) + (res >> k);
#error Invalid PARTORDER
#endif
int res = sum.x + sum.y + sum.z + sum.w;
int k = clamp(clz(1 << PARTORDER) - clz(res), 0, 14); // 27 - clz(res) == clz(16) - clz(res) == log2(res / 16)
#ifdef EXTRAMODE
#if PARTORDER == 3
sum = (chunk[0] >> k) + (chunk[1] >> k);
#elif PARTORDER == 4
sum = (chunk[0] >> k) + (chunk[1] >> k) + (chunk[2] >> k) + (chunk[3] >> k);
#else
#error Invalid PARTORDER
#endif
len[tid] += (k << PARTORDER) + sum.x + sum.y + sum.z + sum.w;
#else
len[tid] += (k << PARTORDER) + (res >> k);
#endif
}
data[tid] = nextData;
}
barrier(CLK_LOCAL_MEM_FENCE);
for (int l = GROUP_SIZE / 32; l > 0; l >>= 1)
for (int l = GROUP_SIZE >> (PARTORDER + 1); l > 0; l >>= 1)
{
if (tid < l)
len[tid] += len[tid + l];
@@ -553,7 +574,7 @@ void cudaEstimateResidual(
}
__kernel __attribute__((reqd_work_group_size(32, 1, 1)))
void cudaChooseBestMethod(
void clChooseBestMethod(
__global FLACCLSubframeTask *tasks,
__global int *residual,
int taskCount
@@ -621,7 +642,7 @@ void cudaChooseBestMethod(
}
__kernel __attribute__((reqd_work_group_size(64, 1, 1)))
void cudaCopyBestMethod(
void clCopyBestMethod(
__global FLACCLSubframeTask *tasks_out,
__global FLACCLSubframeTask *tasks,
int count
@@ -636,7 +657,7 @@ void cudaCopyBestMethod(
}
__kernel __attribute__((reqd_work_group_size(64, 1, 1)))
void cudaCopyBestMethodStereo(
void clCopyBestMethodStereo(
__global FLACCLSubframeTask *tasks_out,
__global FLACCLSubframeTask *tasks,
int count
@@ -690,7 +711,7 @@ void cudaCopyBestMethodStereo(
// get_group_id(0) == task index
__kernel __attribute__((reqd_work_group_size(GROUP_SIZE, 1, 1)))
void cudaEncodeResidual(
void clEncodeResidual(
__global int *output,
__global int *samples,
__global FLACCLSubframeTask *tasks
@@ -756,10 +777,10 @@ void cudaEncodeResidual(
}
}
// get_group_id(0) == partition index
// get_group_id(0) == partition index / (GROUP_SIZE / 16)
// get_group_id(1) == task index
__kernel __attribute__((reqd_work_group_size(GROUP_SIZE, 1, 1)))
void cudaCalcPartition(
void clCalcPartition(
__global int *partition_lengths,
__global int *residual,
__global FLACCLSubframeTask *tasks,
@@ -767,51 +788,53 @@ void cudaCalcPartition(
int psize // == task.blocksize >> max_porder?
)
{
__local int data[GROUP_SIZE];
__local int length[GROUP_SIZE / 16][16];
__local int pl[(GROUP_SIZE / 16)][15];
__local FLACCLSubframeData task;
const int tid = get_local_id(0);
if (tid < sizeof(task) / sizeof(int))
((__local int*)&task)[tid] = ((__global int*)(&tasks[get_group_id(1)]))[tid];
barrier(CLK_LOCAL_MEM_FENCE);
int k = tid % 16;
int x = tid / 16;
int sum = 0;
for (int pos0 = 0; pos0 < psize; pos0 += GROUP_SIZE)
if (tid < (GROUP_SIZE / 16))
{
int offs = get_group_id(0) * psize + pos0 + tid;
// fetch residual
int s = (offs >= task.residualOrder && pos0 + tid < psize) ? residual[task.residualOffs + offs] : 0;
// convert to unsigned
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
for (int pos = 0; pos < psize && pos < GROUP_SIZE; pos += GROUP_SIZE / 16)
sum += data[pos + x] >> k;
barrier(CLK_LOCAL_MEM_FENCE);
for (int k = 0; k <= 14; k++)
pl[tid][k] = 0;
}
length[x][k] = min(0x7fffff, sum);
barrier(CLK_LOCAL_MEM_FENCE);
if (x == 0)
int start = get_group_id(0) * psize * (GROUP_SIZE / 16);
int end = min(start + psize * (GROUP_SIZE / 16), task.blocksize);
for (int offs = start + tid; offs < end; offs += GROUP_SIZE)
{
for (int i = 1; i < GROUP_SIZE / 16; i++)
length[0][k] += length[i][k];
// 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(0x7fffff,length[0][k]) + (psize - task.residualOrder * (get_group_id(0) == 0)) * (k + 1);
// fetch residual
int s = (offs >= task.residualOrder && offs < end) ? residual[task.residualOffs + offs] : 0;
// convert to unsigned
s = clamp(s, -0x7fffff, 0x7fffff);
s = (s << 1) ^ (s >> 31);
// calc number of unary bits for each residual sample with each rice paramater
int part = (offs - start) / psize;
for (int k = 0; k <= 14; k++)
atom_add(&pl[part][k], s >> k);
//pl[part][k] += s >> k;
}
barrier(CLK_LOCAL_MEM_FENCE);
int part = get_group_id(0) * (GROUP_SIZE / 16) + tid;
if (tid < (GROUP_SIZE / 16) && part < (1 << max_porder))
{
for (int k = 0; k <= 14; k++)
{
// output length
const int pos = (15 << (max_porder + 1)) * get_group_id(1) + (k << (max_porder + 1));
partition_lengths[pos + part] = min(0x7fffff, pl[tid][k]) + select(psize, psize - task.residualOrder, part == 0) * (k + 1);
// if (get_group_id(1) == 0)
//printf("pl[%d][%d] == %d\n", k, part, min(0x7fffff, pl[k][tid]) + (psize - task.residualOrder * (part == 0)) * (k + 1));
}
}
}
// get_group_id(0) == task index
__kernel __attribute__((reqd_work_group_size(GROUP_SIZE, 1, 1)))
void cudaCalcPartition16(
void clCalcPartition16(
__global int *partition_lengths,
__global int *residual,
__global int *samples,
@@ -887,6 +910,9 @@ void cudaCalcPartition16(
s = clamp(s, -0x7fffff, 0x7fffff);
// convert to unsigned
res[tid] = (s << 1) ^ (s >> 31);
// for (int k = 0; k < 15; k++) atom_add(&pl[x][k], s >> k);
barrier(CLK_LOCAL_MEM_FENCE);
data[tid] = nextData;
@@ -906,22 +932,22 @@ void cudaCalcPartition16(
// get_group_id(0) == k
// get_group_id(1) == task index
__kernel __attribute__((reqd_work_group_size(128, 1, 1)))
void cudaSumPartition(
void clSumPartition(
__global int* partition_lengths,
int max_porder
)
{
__local int data[512]; // max_porder <= 8, data length <= 1 << 9.
__local int data[256]; // 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;
int2 pl = get_local_id(0) * 2 < (1 << max_porder) ? *(__global int2*)&partition_lengths[pos + get_local_id(0) * 2] : 0;
data[get_local_id(0)] = pl.x + pl.y;
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)
int out_pos = (1 << (max_porder - 1)) + get_local_id(0);
for (int bs = 1 << (max_porder - 2); bs > 0; bs >>= 1)
{
if (get_local_id(0) < bs) data[out_pos] = data[in_pos] + data[in_pos + 1];
in_pos += bs << 1;
@@ -929,131 +955,96 @@ void cudaSumPartition(
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)];
partition_lengths[pos + (1 << max_porder) + get_local_id(0)] = data[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)];
partition_lengths[pos + (1 << max_porder) + get_local_size(0) + get_local_id(0)] = data[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(0) == chunk index (chunk size is GROUP_SIZE, total task size is (2 << max_porder))
// get_group_id(1) == task index
__kernel __attribute__((reqd_work_group_size(GROUP_SIZE, 1, 1)))
void cudaFindRiceParameter(
void clFindRiceParameter(
__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));
const int parts = min(GROUP_SIZE, 2 << max_porder);
const int pos = (15 << (max_porder + 1)) * get_group_id(1) + get_group_id(0) * GROUP_SIZE + tid;
// 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 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)
{
int best_l = partition_lengths[pos];
int best_k = 0;
for (int k = 1; k <= 14; k++)
{
int l = partition_lengths[pos + (k << (max_porder + 1))];
best_k = select(best_k, k, l < best_l);
best_l = min(best_l, l);
}
// output rice parameter
rice_parameters[(get_group_id(1) << (max_porder + 2)) + get_group_id(0) * parts + tid] = shared.index[tid];
rice_parameters[(get_group_id(1) << (max_porder + 2)) + get_group_id(0) * GROUP_SIZE + tid] = best_k;
// output length
rice_parameters[(get_group_id(1) << (max_porder + 2)) + (1 << (max_porder + 1)) + get_group_id(0) * parts + tid] = shared.length[tid];
rice_parameters[(get_group_id(1) << (max_porder + 2)) + (1 << (max_porder + 1)) + get_group_id(0) * GROUP_SIZE + tid] = best_l;
}
}
// get_group_id(0) == task index
__kernel __attribute__((reqd_work_group_size(GROUP_SIZE, 1, 1)))
void cudaFindPartitionOrder(
void clFindPartitionOrder(
__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 int partlen[9];
__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)];
if (get_local_id(0) < 9)
partlen[get_local_id(0)] = 0;
barrier(CLK_LOCAL_MEM_FENCE);
// fetch partition lengths
for (int offs = 0; offs < (2 << max_porder); offs += GROUP_SIZE)
{
if (offs + get_local_id(0) < (2 << max_porder) - 1)
{
int len = rice_parameters[pos + offs + get_local_id(0)];
int porder = 31 - clz((2 << max_porder) - 1 - offs - get_local_id(0));
atom_add(&partlen[porder], len);
}
}
barrier(CLK_LOCAL_MEM_FENCE);
for (int porder = max_porder; porder >= 0; porder--)
int best_length = partlen[0] + 4;
int best_porder = 0;
for (int porder = 1; porder <= max_porder; 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);
int length = (4 << porder) + partlen[porder];
best_porder = select(best_porder, porder, length < best_length);
best_length = min(best_length, length);
}
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);
//atom_min(shared.index[get_local_id(0)],);
int l1 = get_local_id(0) <= max_porder ? shared.length[get_local_id(0)] : 0xfffffff;
for (int l = 8; l > 0; l >>= 1)
{
if (get_local_id(0) < l)
{
int l2 = shared.length[get_local_id(0) + l];
shared.index[get_local_id(0)] = shared.index[get_local_id(0) + select(0, l, l2 < l1)];
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)
{
tasks[get_group_id(0)].data.porder = best_porder;
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 == Fixed ? task.residualOrder * obits + 6 + best_length :
task.type == LPC ? task.residualOrder * obits + 6 + best_length + 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)];
for (int offs = 0; offs < (1 << best_porder); offs += GROUP_SIZE)
if (offs + get_local_id(0) < (1 << best_porder))
best_rice_parameters[(get_group_id(0) << max_porder) + offs + get_local_id(0)] = rice_parameters[pos - (2 << best_porder) + offs + get_local_id(0)];
// FIXME: should be bytes?
}
#endif