root / Epidevomath / vector8.py @ 109
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import numpy as np |
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import pyopencl as cl |
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import pyopencl.array as cl_array |
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from numpy.random import randint as nprnd |
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deviceID = 0
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platformID = 0
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workGroup=(1,1) |
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N = 32768
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MyData = np.zeros(N, dtype=cl_array.vec.float8) |
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dev = cl.get_platforms()[platformID].get_devices()[deviceID] |
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ctx = cl.Context([dev]) |
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queue = cl.CommandQueue(ctx) |
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mf = cl.mem_flags |
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clData = cl.Buffer(ctx, mf.READ_WRITE, MyData.nbytes) |
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MyRoutines = cl.Program(ctx, """
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#define znew ((z=36969*(z&65535)+(z>>16))<<16)
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#define wnew ((w=18000*(w&65535)+(w>>16))&65535)
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#define MWC (znew+wnew)
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#define SHR3 (jsr=(jsr=(jsr=jsr^(jsr<<17))^(jsr>>13))^(jsr<<5))
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#define CONG (jcong=69069*jcong+1234567)
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#define KISS ((MWC^CONG)+SHR3)
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#define MWCfp MWC * 2.328306435454494e-10f
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#define KISSfp KISS * 2.328306435454494e-10f
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#define SHR3fp SHR3 * 2.328306435454494e-10f
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#define CONGfp CONG * 2.328306435454494e-10f
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#define LENGTH 1.
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#define PI 3.14159265359
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#define SMALL_NUM 0.0000001
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__kernel void SplutterSpace(__global float8* clData,
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uint seed_z,uint seed_w)
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{
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int gid = get_global_id(0);
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uint z=seed_z+(uint)gid;
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uint w=seed_w-(uint)gid;
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clData[gid].s01234567 = (float8) (MWCfp,MWCfp,MWCfp,0.,0.,0.,0.,0.);
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}
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__kernel void ExtendSegment(__global float8* clData,
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uint seed_z,uint seed_w)
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{
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int gid = get_global_id(0);
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uint z=seed_z+(uint)gid;
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uint w=seed_w-(uint)gid;
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float theta=MWCfp*PI;
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float phi=MWCfp*PI*2.;
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float sinTheta=sin(theta);
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clData[gid].s4=clData[gid].s0+LENGTH*sinTheta*cos(phi);
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clData[gid].s5=clData[gid].s1+LENGTH*sinTheta*sin(phi);
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clData[gid].s6=clData[gid].s2+LENGTH*cos(theta);
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}
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__kernel void EstimateLength(__global float8* clData,__global float* clSize)
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{
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int gid = get_global_id(0);
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clSize[gid]=distance(clData[gid].lo,clData[gid].hi);
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}
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// Get from http://geomalgorithms.com/a07-_distance.html
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__kernel void ShortestDistance(__global float8* clData,__global float* clDistance)
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{
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int gidx = get_global_id(0);
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int ggsz = get_global_size(0);
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int gidy = get_global_id(1);
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float4 u = clData[gidx].hi - clData[gidx].lo;
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float4 v = clData[gidy].hi - clData[gidy].lo;
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float4 w = clData[gidx].lo - clData[gidy].lo;
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float a = dot(u,u); // always >= 0
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float b = dot(u,v);
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float c = dot(v,v); // always >= 0
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float d = dot(u,w);
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float e = dot(v,w);
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float D = a*c - b*b; // always >= 0
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float sc, sN, sD = D; // sc = sN / sD, default sD = D >= 0
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float tc, tN, tD = D; // tc = tN / tD, default tD = D >= 0
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// compute the line parameters of the two closest points
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if (D < SMALL_NUM) { // the lines are almost parallel
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sN = 0.0; // force using point P0 on segment S1
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sD = 1.0; // to prevent possible division by 0.0 later
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tN = e;
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tD = c;
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}
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else { // get the closest points on the infinite lines
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sN = (b*e - c*d);
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tN = (a*e - b*d);
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if (sN < 0.0) { // sc < 0 => the s=0 edge is visible
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sN = 0.0;
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tN = e;
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tD = c;
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}
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else if (sN > sD) { // sc > 1 => the s=1 edge is visible
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sN = sD;
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tN = e + b;
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tD = c;
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}
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}
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if (tN < 0.0) { // tc < 0 => the t=0 edge is visible
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tN = 0.0;
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// recompute sc for this edge
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if (-d < 0.0)
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sN = 0.0;
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else if (-d > a)
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sN = sD;
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else {
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sN = -d;
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sD = a;
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}
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}
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else if (tN > tD) { // tc > 1 => the t=1 edge is visible
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tN = tD;
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// recompute sc for this edge
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if ((-d + b) < 0.0)
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sN = 0;
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else if ((-d + b) > a)
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sN = sD;
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else {
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sN = (-d + b);
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sD = a;
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}
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}
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// finally do the division to get sc and tc
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sc = (fabs(sN) < SMALL_NUM ? 0.0 : sN / sD);
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tc = (fabs(tN) < SMALL_NUM ? 0.0 : tN / tD);
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// get the difference of the two closest points
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float4 dP = w + (sc * u) - (tc * v); // = S1(sc) - S2(tc)
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clDistance[ggsz*gidy+gidx]=length(dP); // return the closest distance
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}
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""").build()
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print 'Tous au meme endroit',MyData |
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MyRoutines.SplutterSpace(queue, (N,1), None, clData, numpy.uint32(nprnd(2**32)),numpy.uint32(nprnd(2**32))) |
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cl.enqueue_copy(queue, MyData, clData) |
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print 'Tous distribues',MyData |
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MyRoutines.ExtendSegment(queue, (N,1), None, clData,numpy.uint32(nprnd(2**32)),numpy.uint32(nprnd(2**32))) |
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cl.enqueue_copy(queue, MyData, clData) |
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print 'Tous avec leur extremite',MyData |
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MySize = np.zeros(len(MyData), dtype=numpy.float32)
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clSize = cl.Buffer(ctx, mf.READ_WRITE, MySize.nbytes) |
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MyRoutines.EstimateLength(queue, (N,1), None, clData, clSize) |
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cl.enqueue_copy(queue, MySize, clSize) |
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print 'La distance de chacun avec son extremite',MySize |
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MyDistance = np.zeros(len(MyData)*len(MyData), dtype=numpy.float32) |
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clDistance = cl.Buffer(ctx, mf.READ_WRITE, MyDistance.nbytes) |
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MyRoutines.ShortestDistance(queue, (N,N), None, clData, clDistance)
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cl.enqueue_copy(queue, MyDistance, clDistance) |
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MyDistance=numpy.reshape(MyDistance,(N,N)) |
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print 'La distance de chacun',MyDistance |
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