用OpenCL實現HEVC中ME模塊的測試數據分析

使用opencl來實現編碼算法中運動搜索模塊！

下面測試數據時在GTX570上的測試結果：

LCU為32x32, 100幀720P, CPU上純C算法使用搜索時間是67s, GPU上是0.915s

LCU為16x16, CPU 是76.8s, GPU上是1.6s

LCU為8x8, CPU 是82.5s, GPU上是4.2s

同樣的程序， CPU改為SSE實現， GPU做一個小的改動， 使用縮減算法！ 結果如下：

從上面數據可以看出， sse 比C語言快5倍左右， 新的GPU kenel快了20%左右， 其中LCU為8x8的快了好幾倍！

綜合看來

OPENCL實現 比C語言實現接近100倍的級別， 比SSE快了接近20倍左右！

另外提一句， 如果OPENCL不適用__local 內存的話， 會慢一半！

下面貼出部分代碼供參考：

#define SearchRange 16
#define Edge_SIZE_T 48
//32x32 version of kernel
__kernel void opencl_me_32x32(const __global short* p_ref, __global short* p_cur, __global int* outputBuf, __local int* local_refBuf, __local int* local_curBuf, __local int* mv_cost)
{
int searchrange = SearchRange;
int edeg = Edge_SIZE_T;
int width = get_global_size(0);
int height = get_global_size(1);
int block_w = get_local_size(0);
int block_h = get_local_size(1);
int local_x = get_local_id(0);
int local_y = get_local_id(1);
int lcu_x = get_group_id(0);
int lcu_y = get_group_id(1);
int stride = width + 2 * edeg;
int lcu_adr_offset = edeg * stride + edeg;
int local_refBuf_stride = block_w + 2 * searchrange;

//LCU blcok adr
lcu_adr_offset += lcu_y * stride * block_h + lcu_x * block_w;
int ref_lcu_adr_offset = lcu_adr_offset - searchrange - searchrange * stride;
//thread adr
int global_thread_adr_offset = local_y * stride + local_x;
int thread_adr_offset = local_y * local_refBuf_stride + local_x;

local_curBuf[local_y * block_w + local_x] = p_cur[lcu_adr_offset + global_thread_adr_offset];

local_refBuf[thread_adr_offset] = p_ref[ref_lcu_adr_offset + global_thread_adr_offset ];
local_refBuf[thread_adr_offset + block_w] = p_ref[ref_lcu_adr_offset + global_thread_adr_offset + block_w];
local_refBuf[thread_adr_offset + local_refBuf_stride * block_h] = p_ref[ref_lcu_adr_offset + global_thread_adr_offset + stride * block_h];
local_refBuf[thread_adr_offset + local_refBuf_stride * block_h + block_w] = p_ref[ref_lcu_adr_offset + global_thread_adr_offset + stride * block_h + block_w];

barrier(CLK_LOCAL_MEM_FENCE);
{
int i;
int uiSum = 0;
for( int i = 0; i < block_h; i++ )
{

計算sad

}
{
比較最小SAD 保存bestcost
}
if((local_y ==0) && (local_x == 0))
{
int best_sad = mv_cost[local_y*2*SearchRange*3 + local_x*3 + 2];
int best_mvx = mv_cost[local_y*2*SearchRange*3 + local_x*3 + 0];
int best_mvy = mv_cost[local_y*2*SearchRange*3 + local_x*3 + 1];
outputBuf[(lcu_y * get_num_groups(0) + lcu_x)*3 + 0] = best_mvx;
outputBuf[(lcu_y * get_num_groups(0) + lcu_x)*3 + 1] = best_mvy;
outputBuf[(lcu_y * get_num_groups(0) + lcu_x)*3 + 2] = best_sad;
//printf("\nxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx = %d, y = %d, sad = %d",best_mvx, best_mvy, best_sad);
}