diff --git a/pyfstat/grid_based_searches.py b/pyfstat/grid_based_searches.py
index 091b2ae2a977f15832ddc23cd0bd8432220ec75a..feeb97a8c7710ba9ebbdb67a6d87b35e82d9f670 100644
--- a/pyfstat/grid_based_searches.py
+++ b/pyfstat/grid_based_searches.py
@@ -438,6 +438,10 @@ class TransientGridSearch(GridSearch):
FstatMap = getattr(self.search, 'FstatMap', None)
thisCand = list(vals) + [detstat]
if getattr(self, 'transientWindowType', None):
+ if self.tCWFstatMapVersion == 'lal':
+ F_mn = FstatMap.F_mn.data
+ else:
+ F_mn = FstatMap.F_mn
if self.outputTransientFstatMap:
tCWfile = os.path.splitext(self.out_file)[0]+'_tCW_%.16f_%.16f_%.16f_%.16g_%.16g.dat' % (vals[2],vals[5],vals[6],vals[3],vals[4]) # freq alpha delta f1dot f2dot
if self.tCWFstatMapVersion == 'lal':
@@ -445,9 +449,8 @@ class TransientGridSearch(GridSearch):
lalpulsar.write_transientFstatMap_to_fp ( fo, FstatMap, windowRange, None )
del fo # instead of lal.FileClose() which is not SWIG-exported
else:
- np.savetxt(tCWfile, 2.0*FstatMap.F_mn, delimiter=' ')
- Fmn = FstatMap.F_mn.data
- maxidx = np.unravel_index(Fmn.argmax(), Fmn.shape)
+ np.savetxt(tCWfile, 2.0*F_mn, delimiter=' ')
+ maxidx = np.unravel_index(F_mn.argmax(), F_mn.shape)
thisCand += [windowRange.t0+maxidx[0]*windowRange.dt0,
windowRange.tau+maxidx[1]*windowRange.dtau]
data.append(thisCand)
diff --git a/pyfstat/pyCUDAkernels/cudaTransientFstatExpWindow.cu b/pyfstat/pyCUDAkernels/cudaTransientFstatExpWindow.cu
new file mode 100644
index 0000000000000000000000000000000000000000..28b9d8e288f142a8d4222adb56709ef561599ef4
--- /dev/null
+++ b/pyfstat/pyCUDAkernels/cudaTransientFstatExpWindow.cu
@@ -0,0 +1,122 @@
+__global__ void cudaTransientFstatExpWindow ( float *input,
+ unsigned int numAtoms,
+ unsigned int TAtom,
+ unsigned int t0_data,
+ unsigned int win_t0,
+ unsigned int win_dt0,
+ unsigned int win_tau,
+ unsigned int win_dtau,
+ unsigned int Fmn_rows,
+ unsigned int Fmn_cols,
+ float *Fmn
+ )
+{
+
+ /* match CUDA thread indexing and high-level (t0,tau) indexing */
+ unsigned int m = blockDim.x * blockIdx.x + threadIdx.x; // t0: row
+ unsigned int n = blockDim.y * blockIdx.y + threadIdx.y; // tau: column
+ /* unraveled 1D index for 2D output array */
+ unsigned int outidx = Fmn_cols * m + n;
+
+ /* hardcoded copy from lalpulsar */
+ unsigned int TRANSIENT_EXP_EFOLDING = 3;
+
+ if ( (m < Fmn_rows) && (n < Fmn_cols) ) {
+
+ /* compute Fstat-atom index i_t0 in [0, numAtoms) */
+ unsigned int TAtomHalf = TAtom/2; // integer division
+ unsigned int t0 = win_t0 + m * win_dt0;
+ int i_tmp = ( t0 - t0_data + TAtomHalf ) / TAtom; // integer round: floor(x+0.5)
+ if ( i_tmp < 0 ) {
+ i_tmp = 0;
+ }
+ unsigned int i_t0 = (unsigned int)i_tmp;
+ if ( i_t0 >= numAtoms ) {
+ i_t0 = numAtoms - 1;
+ }
+
+ /* translate n into an atoms end-index for this search interval [t0, t0+Tcoh],
+ * giving the index range of atoms to sum over
+ */
+ unsigned int tau = win_tau + n * win_dtau;
+
+ /* get end-time t1 of this transient-window search
+ * for given tau, what Tcoh should the exponential window cover?
+ * for speed reasons we want to truncate Tcoh = tau * TRANSIENT_EXP_EFOLDING
+ * with the e-folding factor chosen such that the window-value
+ * is practically negligible after that, where it will be set to 0
+ */
+// unsigned int t1 = lround( win_t0 + TRANSIENT_EXP_EFOLDING * win_tau);
+ unsigned int t1 = t0 + TRANSIENT_EXP_EFOLDING * tau;
+
+ /* compute window end-time Fstat-atom index i_t1 in [0, numAtoms) */
+ i_tmp = ( t1 - t0_data + TAtomHalf ) / TAtom - 1; // integer round: floor(x+0.5)
+ if ( i_tmp < 0 ) {
+ i_tmp = 0;
+ }
+ unsigned int i_t1 = (unsigned int)i_tmp;
+ if ( i_t1 >= numAtoms ) {
+ i_t1 = numAtoms - 1;
+ }
+
+ /* now we have two valid atoms-indices [i_t0, i_t1]
+ * spanning our Fstat-window to sum over
+ */
+
+ float Ad = 0.0f;
+ float Bd = 0.0f;
+ float Cd = 0.0f;
+ float Fa_re = 0.0f;
+ float Fa_im = 0.0f;
+ float Fb_re = 0.0f;
+ float Fb_im = 0.0f;
+
+ unsigned short input_cols = 7; // must match input matrix!
+
+ /* sum up atoms */
+ for ( unsigned int i=i_t0; i<=i_t1; i++ ) {
+
+ unsigned int t_i = t0_data + i * TAtom;
+
+ float win_i = 0.0;
+ if ( t_i >= t0 && t_i <= t1 ) {
+ float x = 1.0 * ( t_i - t0 ) / tau;
+ win_i = exp ( -x );
+ }
+
+ float win2_i = win_i * win_i;
+
+ Ad += input[i*input_cols+0] * win2_i; // a2_alpha
+ Bd += input[i*input_cols+1] * win2_i; // b2_alpha
+ Cd += input[i*input_cols+2] * win2_i; // ab_alpha
+ Fa_re += input[i*input_cols+3] * win_i; // Fa_alpha_re
+ Fa_im += input[i*input_cols+4] * win_i; // Fa_alpha_im
+ Fb_re += input[i*input_cols+5] * win_i; // Fb_alpha_re
+ Fb_im += input[i*input_cols+6] * win_i; // Fb_alpha_im
+
+ }
+
+ /* get determinant */
+ float Dd = ( Ad * Bd - Cd * Cd );
+ float DdInv = 0.0f;
+ /* safety catch as in XLALWeightMultiAMCoeffs():
+ * make it so that in the end F=0 instead of -nan
+ */
+ if ( Dd > 0.0 ) {
+ DdInv = 1.0 / Dd;
+ }
+
+ /* from XLALComputeFstatFromFaFb */
+ float F = DdInv * ( Bd * ( Fa_re*Fa_re + Fa_im*Fa_im )
+ + Ad * ( Fb_re*Fb_re + Fb_im*Fb_im )
+ - 2.0 * Cd * ( Fa_re * Fb_re + Fa_im * Fb_im )
+ );
+
+ /* store result in Fstat-matrix
+ * at unraveled index of element {m,n}
+ */
+ Fmn[outidx] = F;
+
+ } // ( (m < Fmn_rows) && (n < Fmn_cols) )
+
+} // cudaTransientFstatExpWindow()
diff --git a/pyfstat/pyCUDAkernels/cudaTransientFstatRectWindow.cu b/pyfstat/pyCUDAkernels/cudaTransientFstatRectWindow.cu
new file mode 100644
index 0000000000000000000000000000000000000000..276cc2e27cb78ae008e8d74e235675f7510a1fde
--- /dev/null
+++ b/pyfstat/pyCUDAkernels/cudaTransientFstatRectWindow.cu
@@ -0,0 +1,114 @@
+__global__ void cudaTransientFstatRectWindow ( float *input,
+ unsigned int numAtoms,
+ unsigned int TAtom,
+ unsigned int t0_data,
+ unsigned int win_t0,
+ unsigned int win_dt0,
+ unsigned int win_tau,
+ unsigned int win_dtau,
+ unsigned int N_tauRange,
+ float *Fmn
+ )
+{
+
+ /* match CUDA thread indexing and high-level (t0,tau) indexing */
+ // assume 1D block, grid setup
+ unsigned int m = blockDim.x * blockIdx.x + threadIdx.x; // t0: row
+
+ unsigned short input_cols = 7; // must match input matrix!
+
+ /* compute Fstat-atom index i_t0 in [0, numAtoms) */
+ unsigned int TAtomHalf = TAtom/2; // integer division
+ unsigned int t0 = win_t0 + m * win_dt0;
+ int i_tmp = ( t0 - t0_data + TAtomHalf ) / TAtom; // integer round: floor(x+0.5)
+ if ( i_tmp < 0 ) {
+ i_tmp = 0;
+ }
+ unsigned int i_t0 = (unsigned int)i_tmp;
+ if ( i_t0 >= numAtoms ) {
+ i_t0 = numAtoms - 1;
+ }
+
+ float Ad = 0.0f;
+ float Bd = 0.0f;
+ float Cd = 0.0f;
+ float Fa_re = 0.0f;
+ float Fa_im = 0.0f;
+ float Fb_re = 0.0f;
+ float Fb_im = 0.0f;
+ unsigned int i_t1_last = i_t0;
+
+ /* INNER loop over timescale-parameter tau
+ * NOT parallelized so that we can still use the i_t1_last trick
+ * (empirically seems to be faster than 2D CUDA version)
+ */
+ for ( unsigned int n = 0; n < N_tauRange; n ++ ) {
+
+ if ( (m < N_tauRange) && (n < N_tauRange) ) {
+
+ /* translate n into an atoms end-index for this search interval [t0, t0+Tcoh],
+ * giving the index range of atoms to sum over
+ */
+ unsigned int tau = win_tau + n * win_dtau;
+
+ /* get end-time t1 of this transient-window search */
+ unsigned int t1 = t0 + tau;
+
+ /* compute window end-time Fstat-atom index i_t1 in [0, numAtoms) */
+ i_tmp = ( t1 - t0_data + TAtomHalf ) / TAtom - 1; // integer round: floor(x+0.5)
+ if ( i_tmp < 0 ) {
+ i_tmp = 0;
+ }
+ unsigned int i_t1 = (unsigned int)i_tmp;
+ if ( i_t1 >= numAtoms ) {
+ i_t1 = numAtoms - 1;
+ }
+
+ /* now we have two valid atoms-indices [i_t0, i_t1]
+ * spanning our Fstat-window to sum over
+ */
+
+ for ( unsigned int i = i_t1_last; i <= i_t1; i ++ ) {
+ /* sum up atoms,
+ * special optimiziation in the rectangular-window case:
+ * just add on to previous tau values,
+ * ie re-use the sum over [i_t0, i_t1_last] from the pevious tau-loop iteration
+ */
+ Ad += input[i*input_cols+0]; // a2_alpha
+ Bd += input[i*input_cols+1]; // b2_alpha
+ Cd += input[i*input_cols+2]; // ab_alpha
+ Fa_re += input[i*input_cols+3]; // Fa_alpha_re
+ Fa_im += input[i*input_cols+4]; // Fa_alpha_im
+ Fb_re += input[i*input_cols+5]; // Fb_alpha_re
+ Fb_im += input[i*input_cols+6]; // Fb_alpha_im
+ /* keep track of up to where we summed for the next iteration */
+ i_t1_last = i_t1 + 1;
+ }
+
+ /* get determinant */
+ float Dd = ( Ad * Bd - Cd * Cd );
+ float DdInv = 0.0f;
+ /* safety catch as in XLALWeightMultiAMCoeffs():
+ * make it so that in the end F=0 instead of -nan
+ */
+ if ( Dd > 0.0 ) {
+ DdInv = 1.0 / Dd;
+ }
+
+ /* from XLALComputeFstatFromFaFb */
+ float F = DdInv * ( Bd * ( Fa_re*Fa_re + Fa_im*Fa_im )
+ + Ad * ( Fb_re*Fb_re + Fb_im*Fb_im )
+ - 2.0 * Cd * ( Fa_re * Fb_re + Fa_im * Fb_im )
+ );
+
+ /* store result in Fstat-matrix
+ * at unraveled index of element {m,n}
+ */
+ unsigned int outidx = m * N_tauRange + n;
+ Fmn[outidx] = F;
+
+ } // if ( (m < N_tauRange) && (n < N_tauRange) )
+
+ } // for ( unsigned int n = 0; n < N_tauRange; n ++ )
+
+} // cudaTransientFstatRectWindow()
diff --git a/pyfstat/tcw_fstat_map_funcs.py b/pyfstat/tcw_fstat_map_funcs.py
index 7294e0a6ced84f8776d1bafb7fad8b5766420ebd..fd43b289dd660a1ea2a13e6cfb8ac46b92f5eb7c 100644
--- a/pyfstat/tcw_fstat_map_funcs.py
+++ b/pyfstat/tcw_fstat_map_funcs.py
@@ -1,5 +1,7 @@
""" Additional helper functions dealing with transient-CW F(t0,tau) maps """
+import numpy as np
+import os
import logging
# optional imports
@@ -31,15 +33,34 @@ def optional_import ( modulename, shorthand=None ):
return success
+class pyTransientFstatMap(object):
+ '''
+ simplified object class for a F(t0,tau) F-stat map (not 2F!)
+ based on LALSuite's transientFstatMap_t type
+ replacing the gsl matrix with a numpy array
+
+ F_mn: 2D array of 2F values
+ maxF: maximum of F (not 2F!)
+ t0_ML: maximum likelihood transient start time t0 estimate
+ tau_ML: maximum likelihood transient duration tau estimate
+ '''
+
+ def __init__(self, N_t0Range, N_tauRange):
+ self.F_mn = np.zeros((N_t0Range, N_tauRange), dtype=np.float32)
+ self.maxF = float(-1.0) # Initializing to a negative value ensures that we always update at least once and hence return sane t0_d_ML, tau_d_ML even if there is only a single bin where F=0 happens.
+ self.t0_ML = float(0.0)
+ self.tau_ML = float(0.0)
+
+
# dictionary of the actual callable F-stat map functions we support,
# if the corresponding modules are available.
fstatmap_versions = {
'lal': lambda multiFstatAtoms, windowRange:
getattr(lalpulsar,'ComputeTransientFstatMap')
( multiFstatAtoms, windowRange, False ),
- #'pycuda': lambda multiFstatAtoms, windowRange:
- #pycuda_compute_transient_fstat_map
- #( multiFstatAtoms, windowRange )
+ 'pycuda': lambda multiFstatAtoms, windowRange:
+ pycuda_compute_transient_fstat_map
+ ( multiFstatAtoms, windowRange )
}
@@ -51,13 +72,24 @@ def init_transient_fstat_map_features ( ):
each key's value set to True only if
all required modules are importable on this system.
'''
+
features = {}
+
have_lal = optional_import('lal')
have_lalpulsar = optional_import('lalpulsar')
features['lal'] = have_lal and have_lalpulsar
- features['pycuda'] = False
+
+ # import GPU features
+ have_pycuda_drv = optional_import('pycuda.driver', 'drv')
+ have_pycuda_init = optional_import('pycuda.autoinit', 'autoinit')
+ have_pycuda_gpuarray = optional_import('pycuda.gpuarray', 'gpuarray')
+ have_pycuda_tools = optional_import('pycuda.tools', 'cudatools')
+ have_pycuda_compiler = optional_import('pycuda.compiler', 'cudacomp')
+ features['pycuda'] = have_pycuda_drv and have_pycuda_init and have_pycuda_gpuarray and have_pycuda_tools and have_pycuda_compiler
+
logging.debug('Got the following features for transient F-stat maps:')
logging.debug(features)
+
return features
@@ -72,3 +104,222 @@ def call_compute_transient_fstat_map ( version, features, multiFstatAtoms=None,
else:
raise Exception('Transient F-stat map method "{}" not implemented!'.format(version))
return FstatMap
+
+
+def reshape_FstatAtomsVector ( atomsVector ):
+ '''
+ Make a dictionary of ndarrays out of a atoms "vector" structure.
+
+ The input is a "vector"-like structure with times as the higher hierarchical
+ level and a set of "atoms" quantities defined at each timestamp.
+ The output is a dictionary with an entry for each quantity,
+ which is a 1D ndarray over timestamps for that one quantity.
+ '''
+
+ numAtoms = atomsVector.length
+ atomsDict = {}
+ atom_fieldnames = ['timestamp', 'Fa_alpha', 'Fb_alpha',
+ 'a2_alpha', 'ab_alpha', 'b2_alpha']
+ atom_dtypes = [np.uint32, complex, complex,
+ np.float32, np.float32, np.float32]
+ for f, field in enumerate(atom_fieldnames):
+ atomsDict[field] = np.ndarray(numAtoms,dtype=atom_dtypes[f])
+
+ for n,atom in enumerate(atomsVector.data):
+ for field in atom_fieldnames:
+ atomsDict[field][n] = atom.__getattribute__(field)
+
+ atomsDict['Fa_alpha_re'] = np.float32(atomsDict['Fa_alpha'].real)
+ atomsDict['Fa_alpha_im'] = np.float32(atomsDict['Fa_alpha'].imag)
+ atomsDict['Fb_alpha_re'] = np.float32(atomsDict['Fb_alpha'].real)
+ atomsDict['Fb_alpha_im'] = np.float32(atomsDict['Fb_alpha'].imag)
+
+ return atomsDict
+
+
+def get_absolute_kernel_path ( kernel ):
+ pyfstatdir = os.path.dirname(os.path.abspath(os.path.realpath(__file__)))
+ kernelfile = kernel + '.cu'
+ return os.path.join(pyfstatdir,'pyCUDAkernels',kernelfile)
+
+
+def pycuda_compute_transient_fstat_map ( multiFstatAtoms, windowRange ):
+ '''
+ GPU version of the function to compute transient-window "F-statistic map"
+ over start-time and timescale {t0, tau}.
+ Based on XLALComputeTransientFstatMap from LALSuite,
+ (C) 2009 Reinhard Prix, licensed under GPL
+
+ Returns a 2D matrix F_mn,
+ with m = index over start-times t0,
+ and n = index over timescales tau,
+ in steps of dt0 in [t0, t0+t0Band],
+ and dtau in [tau, tau+tauBand]
+ as defined in windowRange input.
+ '''
+
+ if ( windowRange.type >= lalpulsar.TRANSIENT_LAST ):
+ raise ValueError ('Unknown window-type ({}) passed as input. Allowed are [0,{}].'.format(windowRange.type, lalpulsar.TRANSIENT_LAST-1))
+
+ # internal dict for search/setup parameters
+ tCWparams = {}
+
+ # first combine all multi-atoms
+ # into a single atoms-vector with *unique* timestamps
+ tCWparams['TAtom'] = multiFstatAtoms.data[0].TAtom
+ TAtomHalf = int(tCWparams['TAtom']/2) # integer division
+ atoms = lalpulsar.mergeMultiFstatAtomsBinned ( multiFstatAtoms,
+ tCWparams['TAtom'] )
+
+ # make a combined input matrix of all atoms vectors, for transfer to GPU
+ tCWparams['numAtoms'] = atoms.length
+ atomsDict = reshape_FstatAtomsVector(atoms)
+ atomsInputMatrix = np.column_stack ( (atomsDict['a2_alpha'],
+ atomsDict['b2_alpha'],
+ atomsDict['ab_alpha'],
+ atomsDict['Fa_alpha_re'],
+ atomsDict['Fa_alpha_im'],
+ atomsDict['Fb_alpha_re'],
+ atomsDict['Fb_alpha_im'])
+ )
+
+ # actual data spans [t0_data, t0_data + tCWparams['numAtoms'] * TAtom]
+ # in steps of TAtom
+ tCWparams['t0_data'] = int(atoms.data[0].timestamp)
+ tCWparams['t1_data'] = int(atoms.data[tCWparams['numAtoms']-1].timestamp + tCWparams['TAtom'])
+
+ logging.debug('t0_data={:d}, t1_data={:d}'.format(tCWparams['t0_data'],
+ tCWparams['t1_data']))
+ logging.debug('numAtoms={:d}, TAtom={:d}, TAtomHalf={:d}'.format(tCWparams['numAtoms'],
+ tCWparams['TAtom'],
+ TAtomHalf))
+
+ # special treatment of window_type = none
+ # ==> replace by rectangular window spanning all the data
+ if ( windowRange.type == lalpulsar.TRANSIENT_NONE ):
+ windowRange.type = lalpulsar.TRANSIENT_RECTANGULAR
+ windowRange.t0 = tCWparams['t0_data']
+ windowRange.t0Band = 0
+ windowRange.dt0 = tCWparams['TAtom'] # irrelevant
+ windowRange.tau = tCWparams['numAtoms'] * tCWparams['TAtom']
+ windowRange.tauBand = 0;
+ windowRange.dtau = tCWparams['TAtom'] # irrelevant
+
+ """ NOTE: indices {i,j} enumerate *actual* atoms and their timestamps t_i, while the
+ * indices {m,n} enumerate the full grid of values in [t0_min, t0_max]x[Tcoh_min, Tcoh_max] in
+ * steps of deltaT. This allows us to deal with gaps in the data in a transparent way.
+ *
+ * NOTE2: we operate on the 'binned' atoms returned from XLALmergeMultiFstatAtomsBinned(),
+ * which means we can safely assume all atoms to be lined up perfectly on a 'deltaT' binned grid.
+ *
+ * The mapping used will therefore be {i,j} -> {m,n}:
+ * m = offs_i / deltaT = start-time offset from t0_min measured in deltaT
+ * n = Tcoh_ij / deltaT = duration Tcoh_ij measured in deltaT,
+ *
+ * where
+ * offs_i = t_i - t0_min
+ * Tcoh_ij = t_j - t_i + deltaT
+ *
+ """
+
+ # We allocate a matrix {m x n} = t0Range * TcohRange elements
+ # covering the full timerange the transient window-range [t0,t0+t0Band]x[tau,tau+tauBand]
+ tCWparams['N_t0Range'] = int(np.floor( 1.0*windowRange.t0Band / windowRange.dt0 ) + 1)
+ tCWparams['N_tauRange'] = int(np.floor( 1.0*windowRange.tauBand / windowRange.dtau ) + 1)
+ FstatMap = pyTransientFstatMap ( tCWparams['N_t0Range'], tCWparams['N_tauRange'] )
+
+ logging.debug('N_t0Range={:d}, N_tauRange={:d}'.format(tCWparams['N_t0Range'],
+ tCWparams['N_tauRange']))
+
+ if ( windowRange.type == lalpulsar.TRANSIENT_RECTANGULAR ):
+ FstatMap.F_mn = pycuda_compute_transient_fstat_map_rect ( atomsInputMatrix,
+ windowRange,
+ tCWparams )
+ elif ( windowRange.type == lalpulsar.TRANSIENT_EXPONENTIAL ):
+ FstatMap.F_mn = pycuda_compute_transient_fstat_map_exp ( atomsInputMatrix,
+ windowRange,
+ tCWparams )
+ else:
+ raise ValueError('Invalid transient window type {} not in [{}, {}].'.format(windowRange.type, lalpulsar.TRANSIENT_NONE, lalpulsar.TRANSIENT_LAST-1))
+
+ # out of loop: get max2F and ML estimates over the m x n matrix
+ FstatMap.maxF = FstatMap.F_mn.max()
+ maxidx = np.unravel_index ( FstatMap.F_mn.argmax(),
+ (tCWparams['N_t0Range'],
+ tCWparams['N_tauRange']))
+ FstatMap.t0_ML = windowRange.t0 + maxidx[0] * windowRange.dt0
+ FstatMap.tau_ML = windowRange.tau + maxidx[1] * windowRange.dtau
+
+ logging.debug('Done computing transient F-stat map. maxF={}, t0_ML={}, tau_ML={}'.format(FstatMap.maxF , FstatMap.t0_ML, FstatMap.tau_ML))
+
+ return FstatMap
+
+
+def pycuda_compute_transient_fstat_map_rect ( atomsInputMatrix, windowRange, tCWparams ):
+ '''only GPU-parallizing outer loop, keeping partial sums with memory in kernel'''
+
+ # gpu data setup and transfer
+ logging.debug('Initial GPU memory: {}/{} MB free'.format(drv.mem_get_info()[0]/(2.**20),drv.mem_get_info()[1]/(2.**20)))
+ input_gpu = gpuarray.to_gpu ( atomsInputMatrix )
+ Fmn_gpu = gpuarray.GPUArray ( (tCWparams['N_t0Range'],tCWparams['N_tauRange']), dtype=np.float32 )
+ logging.debug('GPU memory with input+output allocated: {}/{} MB free'.format(drv.mem_get_info()[0]/(2.**20), drv.mem_get_info()[1]/(2.**20)))
+
+ # GPU kernel
+ kernel = 'cudaTransientFstatRectWindow'
+ kernelfile = get_absolute_kernel_path ( kernel )
+ partial_Fstat_cuda_code = cudacomp.SourceModule(open(kernelfile,'r').read())
+ partial_Fstat_cuda = partial_Fstat_cuda_code.get_function(kernel)
+ partial_Fstat_cuda.prepare('PIIIIIIIIP')
+
+ # GPU grid setup
+ blockRows = min(1024,tCWparams['N_t0Range'])
+ blockCols = 1
+ gridRows = int(np.ceil(1.0*tCWparams['N_t0Range']/blockRows))
+ gridCols = 1
+ logging.debug('Looking to compute transient F-stat map over a N={}*{}={} (t0,tau) grid...'.format(tCWparams['N_t0Range'],tCWparams['N_tauRange'],tCWparams['N_t0Range']*tCWparams['N_tauRange']))
+
+ # running the kernel
+ logging.debug('Calling kernel with a grid of {}*{}={} blocks of {}*{}={} threads each: {} total threads...'.format(gridRows, gridCols, gridRows*gridCols, blockRows, blockCols, blockRows*blockCols, gridRows*gridCols*blockRows*blockCols))
+ partial_Fstat_cuda.prepared_call ( (gridRows,gridCols), (blockRows,blockCols,1), input_gpu.gpudata, tCWparams['numAtoms'], tCWparams['TAtom'], tCWparams['t0_data'], windowRange.t0, windowRange.dt0, windowRange.tau, windowRange.dtau, tCWparams['N_tauRange'], Fmn_gpu.gpudata )
+
+ # return results to host
+ F_mn = Fmn_gpu.get()
+
+ logging.debug('Final GPU memory: {}/{} MB free'.format(drv.mem_get_info()[0]/(2.**20),drv.mem_get_info()[1]/(2.**20)))
+
+ return F_mn
+
+
+def pycuda_compute_transient_fstat_map_exp ( atomsInputMatrix, windowRange, tCWparams ):
+ '''exponential window, inner and outer loop GPU-parallelized'''
+
+ # gpu data setup and transfer
+ logging.debug('Initial GPU memory: {}/{} MB free'.format(drv.mem_get_info()[0]/(2.**20),drv.mem_get_info()[1]/(2.**20)))
+ input_gpu = gpuarray.to_gpu ( atomsInputMatrix )
+ Fmn_gpu = gpuarray.GPUArray ( (tCWparams['N_t0Range'],tCWparams['N_tauRange']), dtype=np.float32 )
+ logging.debug('GPU memory with input+output allocated: {}/{} MB free'.format(drv.mem_get_info()[0]/(2.**20), drv.mem_get_info()[1]/(2.**20)))
+
+ # GPU kernel
+ kernel = 'cudaTransientFstatExpWindow'
+ kernelfile = get_absolute_kernel_path ( kernel )
+ partial_Fstat_cuda_code = cudacomp.SourceModule(open(kernelfile,'r').read())
+ partial_Fstat_cuda = partial_Fstat_cuda_code.get_function(kernel)
+ partial_Fstat_cuda.prepare('PIIIIIIIIIP')
+
+ # GPU grid setup
+ blockRows = min(32,tCWparams['N_t0Range'])
+ blockCols = min(32,tCWparams['N_tauRange'])
+ gridRows = int(np.ceil(1.0*tCWparams['N_t0Range']/blockRows))
+ gridCols = int(np.ceil(1.0*tCWparams['N_tauRange']/blockCols))
+ logging.debug('Looking to compute transient F-stat map over a N={}*{}={} (t0,tau) grid...'.format(tCWparams['N_t0Range'], tCWparams['N_tauRange'], tCWparams['N_t0Range']*tCWparams['N_tauRange']))
+
+ # running the kernel
+ logging.debug('Calling kernel with a grid of {}*{}={} blocks of {}*{}={} threads each: {} total threads...'.format(gridRows, gridCols, gridRows*gridCols, blockRows, blockCols, blockRows*blockCols, gridRows*gridCols*blockRows*blockCols))
+ partial_Fstat_cuda.prepared_call ( (gridRows,gridCols), (blockRows,blockCols,1), input_gpu.gpudata, tCWparams['numAtoms'], tCWparams['TAtom'], tCWparams['t0_data'], windowRange.t0, windowRange.dt0, windowRange.tau, windowRange.dtau, tCWparams['N_t0Range'], tCWparams['N_tauRange'], Fmn_gpu.gpudata )
+
+ # return results to host
+ F_mn = Fmn_gpu.get()
+
+ logging.debug('Final GPU memory: {}/{} MB free'.format(drv.mem_get_info()[0]/(2.**20),drv.mem_get_info()[1]/(2.**20)))
+
+ return F_mn
diff --git a/setup.py b/setup.py
index a9d69f304d33b723a3894c2f4ab88f2adfc2f737..2f03d835110d223f056e4798043888d9b241d60d 100644
--- a/setup.py
+++ b/setup.py
@@ -7,4 +7,7 @@ setup(name='PyFstat',
author='Gregory Ashton',
author_email='gregory.ashton@ligo.org',
packages=['pyfstat'],
+ include_package_data=True,
+ package_data={'pyfstat': ['pyCUDAkernels/cudaTransientFstatExpWindow.cu',
+ 'pyCUDAkernels/cudaTransientFstatRectWindow.cu']},
)