OpenCL kernel error on Mac OSx
I wrote OpenCL code that works fine on LINUX, but it fails with errors on Mac OSX. Can someone please help me determine why this should be happening. The kernel code is displayed after the error. My core is using double, so I have the corresponding pragma on top. But I don't know why the error is showing the float datatype:
inline float8 __OVERLOAD__ _name(float8 x) { return _default_name(x); } \
^
/System/Library/Frameworks/OpenCL.framework/Versions/A/lib/clang/3.2/include/cl_kernel.h:4606:30: note: candidate function
__CLFN_FD_1FD_FAST_RELAX(__fast_relax_log, native_log, __cl_log);
^
/System/Library/Frameworks/OpenCL.framework/Versions/A/lib/clang/3.2/include/cl_kernel.h:421:29:
note: expanded from macro '__CLFN_FD_1FD_FAST_RELAX'
inline float16 __OVERLOAD__ _name(float16 x){ return _default_name(x); }
^
<program source>:206:19: error: call to '__fast_relax_log' is ambiguous
det_zkinin + log((2.0) * 3.14));
^~~~~~~~~~~~~~~~~
/System/Library/Frameworks/OpenCL.framework/Versions/A/lib/clang/3.2/include/cl_kernel.h:4608:22:
note: expanded from macro 'log'
#define log(__x) __fast_relax_log(__x)
^~~~~~~~~~~~~~~~
/System/Library/Frameworks/OpenCL.framework/Versions/A/lib/clang/3.2/include/cl_kernel.h:4606:30:
note: candidate function
__CLFN_FD_1FD_FAST_RELAX(__fast_relax_log, native_log, __cl_log);
^
/System/Library/Frameworks/OpenCL.framework/Versions/A/lib/clang/3.2/include/cl_kernel.h:416:27:
note: expanded from macro '__CLFN_FD_1FD_FAST_RELAX'
inline float __OVERLOAD__ _name(float x) { return _default_name(x); } \
^
/System/Library/Frameworks/OpenCL.framework/Versions/A/lib/clang/3.2/include/cl_kernel.h:4606:30
note: candidate function
__CLFN_FD_1FD_FAST_RELAX(__fast_relax_log, native_log, __cl_log);
^
^
This is the kernel code:
#pragma OPENCL EXTENSION cl_khr_fp64: enable
__kernel void ckf_kernel2(int dimx, int aligned_dimx,
int numOfCKF, int aligned_ckf,
int iter,
double epsilon,
__global double * yrlists,
__global double * zrlists,
__global double * rlists,
__global double * init_state,
__global double * init_var,
__global double * sing_j,
__global double * covMatrixSum,
__global double * cummulative,
__global double * temp_var,
__global double * x_k_f,
__global double * z_k_j,
__global double * crossCovMatrixSum,
__global double * z_k_f,
__global double * innCovMatrixSum,
__global double * zk_diff,
__global double * reduce_gain_matrix,
__global double * llk
)
{
int ckf_id = get_global_id(0);
if( ckf_id < numOfCKF){
for (int i = 0 ; i < dimx ; i++)
{
for (int idx = 0; idx < dimx * 2 ; idx++)
{
int column = idx % dimx;
int mode = (idx >= dimx) ? -1 : 1;
sing_j[(i * dimx * 2 + idx) * aligned_ckf + ckf_id] = temp_var[(i * dimx + column) * aligned_ckf + ckf_id] * epsilon * mode + init_state[i * aligned_ckf + ckf_id];
}
}
z_k_f[ckf_id] = 0;
innCovMatrixSum[ckf_id] = 0;
for (int idx = 0; idx < dimx * 2 ; idx++)
{
z_k_j[idx * aligned_ckf + ckf_id] = 0;
for (int i = 0 ; i < dimx ; i++)
z_k_j[idx * aligned_ckf + ckf_id] += sing_j[(i * dimx * 2 + idx) * aligned_ckf + ckf_id] * zrlists[iter * aligned_dimx + i ];
z_k_f[ckf_id] += z_k_j[idx* aligned_ckf + ckf_id] ;
innCovMatrixSum[ckf_id] += z_k_j[idx* aligned_ckf + ckf_id] * z_k_j[idx* aligned_ckf + ckf_id];
}
z_k_f[ckf_id] = z_k_f[ckf_id] / (dimx * 2);
innCovMatrixSum[ckf_id] = innCovMatrixSum[ckf_id] / (dimx * 2);
innCovMatrixSum[ckf_id] = (innCovMatrixSum[ckf_id] - z_k_f[ckf_id] *z_k_f[ckf_id]) + rlists[ckf_id];
// calcualte crossCovMatrixSum
for (int idx = 0; idx < dimx; idx ++)
{
crossCovMatrixSum[idx * aligned_ckf + ckf_id] = 0;
for (int i = 0 ; i < 2 * dimx ; i++)
{
crossCovMatrixSum[idx * aligned_ckf + ckf_id] += sing_j[(idx * dimx*2 + i) * aligned_ckf + ckf_id ] * z_k_j[i* aligned_ckf + ckf_id];
}
crossCovMatrixSum[idx * aligned_ckf + ckf_id] = crossCovMatrixSum[idx * aligned_ckf + ckf_id]/ (dimx * 2);
crossCovMatrixSum[idx * aligned_ckf + ckf_id] = crossCovMatrixSum[idx * aligned_ckf + ckf_id] - x_k_f[idx* aligned_ckf + ckf_id] * z_k_f[ckf_id];
}
// calculate zk_diff
int z_check = (int)yrlists[iter];
if (z_check == -1)
zk_diff[ckf_id] = 0;
else
zk_diff[ckf_id] = yrlists[iter] - z_k_f[ckf_id];
// calculate reduce_gain_matrix and (reduce_state_matrix <==> init_state);
for (int idx = 0 ; idx < dimx; idx++)
{
reduce_gain_matrix[idx * aligned_ckf + ckf_id] = (crossCovMatrixSum[idx * aligned_ckf + ckf_id] / innCovMatrixSum[ckf_id]);
init_state[idx * aligned_ckf + ckf_id] = reduce_gain_matrix[idx * aligned_ckf + ckf_id] * zk_diff[ckf_id] + x_k_f[idx* aligned_ckf + ckf_id];
}
for (int idx = 0 ; idx < dimx; idx++)
{
init_var[idx * aligned_ckf + ckf_id ] = covMatrixSum[(idx * dimx + idx) * aligned_ckf + ckf_id] -
reduce_gain_matrix[idx * aligned_ckf + ckf_id] * innCovMatrixSum[ckf_id] *
reduce_gain_matrix[idx * aligned_ckf + ckf_id];
}
double det_zkinin = zk_diff[ckf_id] * zk_diff[ckf_id] * (1.0f /innCovMatrixSum[ckf_id]);
if (innCovMatrixSum[ckf_id] <= 0)
llk[ckf_id] = 0;
else
llk[ckf_id] = 0.5 * ((log(innCovMatrixSum[ckf_id])) +
det_zkinin + log((2.0) * 3.14));
cummulative[ckf_id] += llk[ckf_id];
}
}
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1 answer
I suspect you are trying to run this on an integrated Intel GPU that does not support double precision. I can only reproduce your error on my Macbook Pro if I compile the kernel code for the Intel HD 4000 - it compiles fine when I target the CPU or discrete NVIDIA GPU.
You can check if a device supports double precision by querying the device information parameter CL_DEVICE_DOUBLE_FP_CONFIG
:
cl_device_fp_config cfg;
clGetDeviceInfo(device, CL_DEVICE_DOUBLE_FP_CONFIG, sizeof(cfg), &cfg, NULL);
printf("Double FP config = %llu\n", cfg);
If this function returns a value 0
, then double precision is not supported. This explains why the compiler log only reports float
function variants log
.
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