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Added parallelization. Fixes

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This commit is contained in:
Guilhem Lavaux 2016-11-24 14:35:52 +01:00
parent 3d67b66ae0
commit 9ef2b008b0
3 changed files with 83 additions and 33 deletions
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83
python/_cosmo_bispectrum.cpp
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@ -1,3 +1,4 @@
#include <omp.h>
#include <iostream> #include <iostream>
#include <boost/format.hpp> #include <boost/format.hpp>
#include <boost/multi_array.hpp> #include <boost/multi_array.hpp>
@ -39,6 +40,7 @@ using boost::format;
struct ModeSet struct ModeSet
{ {
size_t N1, N2, N3; size_t N1, N2, N3;
bool omp;
struct TriangleIterator struct TriangleIterator
{ {
@ -78,6 +80,8 @@ struct ModeSet
t.i1 -= N1; t.i1 -= N1;
if (t.i2 >= N2/2) if (t.i2 >= N2/2)
t.i2 -= N2; t.i2 -= N2;
if (t.i3 >= N3/2)
t.i3 -= N3;
return t; return t;
} }
@ -85,18 +89,21 @@ struct ModeSet
double r1 = i1, r2 = i3, r3 = i3; double r1 = i1, r2 = i3, r3 = i3;
return std::sqrt(r1*r1 + r2*r2 + r3*r3); return std::sqrt(r1*r1 + r2*r2 + r3*r3);
} }
void reverse() { i1=-i1; i2=-i2; i3=-i3; }
TriangleIterator& operator*() { return *this; } TriangleIterator& operator*() { return *this; }
}; };
ModeSet(size_t N1_, size_t N2_, size_t N3_) ModeSet(size_t N1_, size_t N2_, size_t N3_, bool do_openmp = false)
: N1(N1_), N2(N2_), N3(N3_) {} : N1(N1_), N2(N2_), N3(N3_),omp(do_openmp) {
}
TriangleIterator begin() const { TriangleIterator begin() const {
TriangleIterator t; TriangleIterator t;
t.i1 = t.i2 = t.i3 = 0; t.i1 = t.i2 = t.i3 = 0;
t.N1 = N1;
t.N2 = N2; t.N2 = N2;
t.N3 = N3; t.N3 = N3;
t.N1 = N1;
return t; return t;
} }
@ -112,6 +119,9 @@ struct ModeSet
}; };
template<typename T>
static T no_conj(const T& a) { return a; }
extern "C" DLL_PUBLIC extern "C" DLL_PUBLIC
void CosmoTool_compute_bispectrum( void CosmoTool_compute_bispectrum(
double *delta_hat, size_t Nx, size_t Ny, size_t Nz, double *delta_hat, size_t Nx, size_t Ny, size_t Nz,
@ -120,50 +130,75 @@ void CosmoTool_compute_bispectrum(
{ {
// First remap to multi_array for easy access // First remap to multi_array for easy access
size_t kNz = Nz/2+1; size_t kNz = Nz/2+1;
int Ntasks = omp_get_max_threads();
boost::multi_array_ref<std::complex<double>, 3> a_delta(reinterpret_cast<std::complex<double>*>(delta_hat), boost::extents[Nx][Ny][kNz]); boost::multi_array_ref<std::complex<double>, 3> a_delta(reinterpret_cast<std::complex<double>*>(delta_hat), boost::extents[Nx][Ny][kNz]);
boost::multi_array_ref<size_t, 3> a_Nt(Ntriangles, boost::extents[Nk][Nk][Nk]); boost::multi_array_ref<size_t, 3> a_Nt(Ntriangles, boost::extents[Nk][Nk][Nk]);
boost::multi_array_ref<std::complex<double>, 3> a_B(reinterpret_cast<std::complex<double>*>(B), boost::extents[Nk][Nk][Nk]); boost::multi_array_ref<std::complex<double>, 3> a_B(reinterpret_cast<std::complex<double>*>(B), boost::extents[Nk][Nk][Nk]);
boost::multi_array<std::complex<double>, 4> b_B(boost::extents[Ntasks][Nk][Nk][Nk]);
boost::multi_array<size_t, 4> b_Nt(boost::extents[Ntasks][Nk][Nk][Nk]);
typedef std::complex<double> CType; typedef std::complex<double> CType;
// First loop over m1 // First loop over m1
for (auto m1 : ModeSet(Nx, Ny, kNz)) { #pragma omp parallel
CType& v1 = a_delta[m1.i1][m1.i2][m1.i3]; {
#pragma omp single
{
for (auto m1 : ModeSet(Nx, Ny, Nz, true)) {
int tid = omp_get_thread_num();
#pragma omp task
{
CType v1 = (m1.i3 >= kNz) ? std::conj(a_delta[m1.i1][m1.i2][(m1.N3-m1.i3)%m1.N3]) : a_delta[m1.i1][m1.i2][m1.i3];
auto rm1 = m1.real();
// Second mode m2 // Second mode m2
for (auto m2 : ModeSet(Nx, Ny, kNz)) { for (auto m2 : ModeSet(Nx, Ny, Nz)) {
// Now derive m3 // Now derive m3
CType& v2 = a_delta[m2.i1][m2.i2][m2.i3]; CType v2 = (m2.i3 >= kNz) ? std::conj(a_delta[m2.i1][m2.i2][(m2.N3-m2.i3)%m2.N3]) : a_delta[m2.i1][m2.i2][m2.i3];
auto m3 = (m1.real()+m2.real()); auto rm2 = m2.real();
auto m3 = (rm1+rm2);
if (!m3.in_box()) if (!m3.in_box())
continue; continue;
size_t q1 = std::floor(m1.norm()/delta_k); size_t q1 = std::floor(rm1.norm()/delta_k);
size_t q2 = std::floor(m2.norm()/delta_k); size_t q2 = std::floor(rm2.norm()/delta_k);
size_t q3 = std::floor(m3.norm()/delta_k); size_t q3 = std::floor(m3.norm()/delta_k);
if (q1 >= Nk || q2 >= Nk || q3 >= Nk)
if (q1 > Nk || q2 > Nk || q3 > Nk)
continue; continue;
CType prod = v1*v2; CType prod = v1*v2;
bool do_conj = false;
// We use hermitic symmetry to get -m3, it is just the mode in m3 but conjugated. // We use hermitic symmetry to get -m3, it is just the mode in m3 but conjugated.
m3.reverse();
if (m3.i3 > 0) { if (m3.i3 > 0) {
if (m3.i1 < 0) m3.i1 += m3.N1; assert(m3.i3 < kNz);
if (m3.i2 < 0) m3.i2 += m3.N2;
prod *= std::conj(a_delta[m3.i1][m3.i2][m3.i3]);
} else { } else {
m3.i1 = -m3.i1; m3.i3 = -m3.i3;
m3.i2 = -m3.i2; do_conj = !do_conj;
if (m3.i1 < 0) m3.i1 += m3.N1;
if (m3.i2 < 0) m3.i2 += m3.N2;
prod *= a_delta[m3.i1][m3.i2][m3.i3];
} }
m3.i1 = (m3.N1 - m1.i1)%m3.N1;
m3.i2 = (m3.N2 - m3.i2)%m3.N2;
// a_Nt[q1][q2][q3] ++; if (do_conj)
// a_B[q1][q2][q3] += prod; prod *= std::conj(a_delta[m3.i1][m3.i2][m3.i3]);
else
prod *= (a_delta[m3.i1][m3.i2][m3.i3]);
b_Nt[tid][q1][q2][q3] ++;
b_B[tid][q1][q2][q3] += prod;
} }
} }
} }
}
}
for (int tid = 0; tid < Ntasks; tid++)
for (auto m1 : ModeSet(Nk, Nk, Nk)) {
a_Nt[m1.i1][m1.i2][m1.i3] += b_Nt[tid][m1.i1][m1.i2][m1.i3];
a_B[m1.i1][m1.i2][m1.i3] += b_B[tid][m1.i1][m1.i2][m1.i3];
}
}
extern "C" DLL_PUBLIC extern "C" DLL_PUBLIC
@ -185,10 +220,10 @@ void CosmoTool_compute_powerspectrum(
size_t q1 = std::floor(m1.norm()/delta_k); size_t q1 = std::floor(m1.norm()/delta_k);
if (q1 > Nk) if (q1 >= Nk)
continue; continue;
a_Nc[q1] ++; a_Nc[q1] ++;
a_P[q1] += std::norm(v1); a_P[q1] += std::norm(v1);
} }
} }

14
python/cosmotool/bispectrum.py
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@ -11,6 +11,11 @@ try:
double *delta_hat, size_t Nx, size_t Ny, size_t Nz, double *delta_hat, size_t Nx, size_t Ny, size_t Nz,
size_t *Ntriangles, size_t *Ntriangles,
double* B, double delta_k, size_t Nk ) ; double* B, double delta_k, size_t Nk ) ;
void CosmoTool_compute_powerspectrum(
double *delta_hat, size_t Nx, size_t Ny, size_t Nz,
size_t *Ncounts,
double* P, double delta_k, size_t Nk );
"""); """);
_pathlib = os.path.dirname(os.path.abspath(__file__)) _pathlib = os.path.dirname(os.path.abspath(__file__))
@ -42,6 +47,7 @@ def bispectrum(delta, delta_k, Nk, fourier=True):
if not fourier: if not fourier:
delta = np.fft.rfftn(delta) delta = np.fft.rfftn(delta)
N1,N2,N3 = delta.shape N1,N2,N3 = delta.shape
rN3 = (N3-1)*2
delta_hat_buf = np.empty((N1*N2*N3*2),dtype=np.double) delta_hat_buf = np.empty((N1*N2*N3*2),dtype=np.double)
delta_hat_buf[::2] = delta.real.ravel() delta_hat_buf[::2] = delta.real.ravel()
delta_hat_buf[1::2] = delta.imag.ravel() delta_hat_buf[1::2] = delta.imag.ravel()
@ -54,16 +60,16 @@ def bispectrum(delta, delta_k, Nk, fourier=True):
else: else:
raise RuntimeError("Internal error, do not know how to map size_t") raise RuntimeError("Internal error, do not know how to map size_t")
B_buf = np.zeros((Nk*Nk*Nk*4), dtype=np.double) B_buf = np.zeros((Nk*Nk*Nk*2), dtype=np.double)
_lib.CosmoTool_compute_bispectrum( \ _lib.CosmoTool_compute_bispectrum( \
_ffi.cast("double *", delta_hat_buf.ctypes.data), \ _ffi.cast("double *", delta_hat_buf.ctypes.data), \
N1, N2, N3, \ N1, N2, rN3, \
_ffi.cast("size_t *", triangle_buf.ctypes.data), \ _ffi.cast("size_t *", triangle_buf.ctypes.data), \
_ffi.cast("double *", B_buf.ctypes.data), \ _ffi.cast("double *", B_buf.ctypes.data), \
delta_k, \ delta_k, \
Nk) Nk)
B_buf = B_buf.reshape((Nk,Nk,Nk,4)) B_buf = B_buf.reshape((Nk,Nk,Nk,2))
return triangle_buf, B_buf[...,0]+1j*B_buf[...,1] return triangle_buf, B_buf[...,0]+1j*B_buf[...,1]
def powerspectrum(delta, delta_k, Nk, fourier=True): def powerspectrum(delta, delta_k, Nk, fourier=True):
@ -102,7 +108,7 @@ def powerspectrum(delta, delta_k, Nk, fourier=True):
B_buf = np.zeros((Nk,), dtype=np.double) B_buf = np.zeros((Nk,), dtype=np.double)
_lib.CosmoTool_compute_bispectrum( \ _lib.CosmoTool_compute_powerspectrum( \
_ffi.cast("double *", delta_hat_buf.ctypes.data), \ _ffi.cast("double *", delta_hat_buf.ctypes.data), \
N1, N2, N3, \ N1, N2, N3, \
_ffi.cast("size_t *", count_buf.ctypes.data), \ _ffi.cast("size_t *", count_buf.ctypes.data), \

17
python_sample/test_bispectrum.py
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@ -1,10 +1,19 @@
import numpy as np import numpy as np
import cosmotool as ct import cosmotool as ct
f=0.01 N=32
d=np.random.normal(size=(16,16,16)) f=0.10
d=np.random.normal(size=(N,)*3)
rho = d + f *(d*d - np.average(d*d)) rho = d + f *(d*d - np.average(d*d))
B = ct.bispectrum(rho, 1, 16, fourier=False) B = ct.bispectrum(rho, 1, N, fourier=False)
P = ct.powerspectrum(rho, 1, 16, fourier=False) P = ct.powerspectrum(rho, 1, N, fourier=False)
PP = P[1]/P[0]/N**3
x = PP[:,None,None] * PP[None,:,None] + PP[:,None,None]*PP[None,None,:] + PP[None,:,None]*PP[None,None,:]
BB = B[1]/B[0]/N**6
y = BB/x