2010-09-01 13:02:12 -04:00
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"""
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Contains the PTX fragments which will drive the device.
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"""
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2010-08-28 16:56:05 -04:00
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import os
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import time
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2010-09-06 11:18:20 -04:00
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import struct
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2010-08-28 16:56:05 -04:00
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import pycuda.driver as cuda
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import numpy as np
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2010-10-09 11:18:58 -04:00
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from pyptx import ptx, run, util
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2010-09-11 13:15:36 -04:00
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from cuburn.variations import Variations
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2010-08-28 16:56:05 -04:00
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2010-10-07 11:21:43 -04:00
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class IterThread(object):
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2010-10-09 11:18:58 -04:00
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def __init__(self, entry, features):
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self.features = features
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self.mwc = MWCRNG(entry)
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self.cp = util.DataStream(entry)
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self.vars = Variations(features)
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2010-09-06 11:18:20 -04:00
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2010-10-09 11:18:58 -04:00
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entry.add_param('u32', 'num_cps')
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entry.add_ptr_param('u32', 'cp_started_count')
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entry.add_ptr_param('u8', 'cp_data')
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2010-09-06 11:18:20 -04:00
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2010-10-09 11:18:58 -04:00
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with entry.body():
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self.entry_body(entry)
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2010-09-03 00:52:27 -04:00
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2010-10-09 11:18:58 -04:00
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def entry_body(self, entry):
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e, r, o, m, p, s = entry.locals
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# Index of this CTA's current CP
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e.declare_mem('shared', 'u32', 'cp_idx')
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2010-09-07 14:54:50 -04:00
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# Number of samples that have been generated so far in this CTA
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# If this number is negative, we're still fusing points, so this
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# behaves slightly differently (see ``fuse_loop_start``)
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2010-09-12 11:09:47 -04:00
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# TODO: replace (or at least simplify) this logic
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2010-10-09 11:18:58 -04:00
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e.declare_mem('shared', 'f32', 'num_samples')
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# The per-warp transform selection indices
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e.declare_mem('shared', 'f32', 'xf_sel', e.nwarps_cta)
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# TODO: re-add this logic using the printf formatter.
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#mem.local.u32('l_num_rounds')
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#mem.local.u32('l_num_writes')
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#op.st.local.u32(addr(l_num_rounds), 0)
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#op.st.local.u32(addr(l_num_writes), 0)
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# Declare IFS-space coordinates for doing iterations
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r.x, r.y, r.color = r.f32(), r.f32(), r.f32()
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r.x, r.y = self.mwc.next_f32_11(), self.mwc.next_f32_11()
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r.color = self.mwc.next_f32_01()
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# This thread's sample's good/bad/fusing state
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r.consec_bad = r.f32(-self.features.fuse)
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e.comment("The main loop entry point")
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cp_loop_start = e.label()
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with s.tid_x == 0:
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o.st(m.cp_idx.addr, o.atom.add(p.cp_started_count[0], 1))
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o.st(m.num_samples.addr, 0)
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e.comment("Load the CP index in all threads")
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o.bar.sync(0)
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cp_idx = o.ld.volatile(m.cp_idx.addr)
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e.comment("Check to see if this CP is valid (if not, we're done)")
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all_cps_done = e.forward_label()
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with cp_idx < p.num_cps:
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o.bra.uni(all_cps_done)
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self.cp.addr = p.cp_data[cp_idx * self.cp.stream_size]
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loop_start = e.forward_label()
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with s.tid_x < e.nwarps_cta:
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o.bra(loop_start)
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e.comment("Choose the xform for each warp")
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choose_xform = e.label()
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o.st.volatile(m.xf_sel[s.tid_x], self.mwc.next_f32_01())
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e.declare_label(loop_start)
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e.comment("Execute the xform given by xf_sel")
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xf_labels = [e.forward_label() for xf in self.features.xforms]
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xf_sel = o.ld.volatile(m.xf_sel[s.tid_x >> 5])
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for i, xf in enumerate(self.features.xforms):
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xf_density = self.cp.get.f32('cp.xforms[%d].cweight'%xf.id)
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with xf_density <= xf_sel:
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o.bra.uni(xf_labels[i])
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e.comment("This code should be unreachable")
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o.trap()
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xforms_done = e.forward_label()
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for i, xf in enumerate(self.features.xforms):
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e.declare_label(xf_labels[i])
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r.x, r.y, r.color = self.vars.apply_xform(
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e, self.cp, r.x, r.y, r.color, xf.id)
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o.bra.uni(xforms_done)
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e.comment("Determine write location, and whether point is valid")
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e.declare_label(xforms_done)
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histidx, is_valid = self.camera.get_index(r.x, r.y)
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is_valid &= (r.consec_bad >= 0)
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e.comment("Scatter point to pointbuffer")
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self.hist.scatter(histidx, r.color, 0, is_valid)
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done_picking_new_point = e.forward_label()
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with ~is_valid:
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r.consec_bad += 1
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with r.consec_bad < self.features.max_bad:
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o.bra(done_picking_new_point)
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e.comment("If too many consecutive bad values, pick a new point")
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r.x, r.y = self.mwc.next_f32_11(), self.mwc.next_f32_11()
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r.color = self.mwc.next_f32_01()
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r.consec_bad = -self.features.fuse
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e.declare_label(done_picking_new_point)
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e.comment("Determine number of good samples, and whether we're done")
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num_samples = o.ld(m.num_samples)
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num_samples += o.bar.red.popc(0, is_valid)
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with s.tid_x == 0:
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o.st(m.num_samples, num_samples)
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with num_samples >= self.cp.get('nsamples'):
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o.bra.uni(cp_loop_start)
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2010-09-03 00:52:27 -04:00
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2010-09-12 00:17:18 -04:00
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comment('Shuffle points between threads')
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2010-09-12 23:45:38 -04:00
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shuf.shuffle(x, y, color, consec_bad)
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2010-09-12 00:17:18 -04:00
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2010-10-09 11:18:58 -04:00
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with s.tid_x < e.nwarps_cta:
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o.bra(choose_xform)
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o.bra(loop_start)
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e.declare_label(all_cps_done)
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2010-09-03 00:52:27 -04:00
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2010-09-06 11:18:20 -04:00
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def upload_cp_stream(self, ctx, cp_stream, num_cps):
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cp_array_dp, cp_array_l = ctx.mod.get_global('g_cp_array')
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assert len(cp_stream) <= cp_array_l, "Stream too big!"
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2010-09-11 13:15:36 -04:00
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cuda.memcpy_htod(cp_array_dp, cp_stream)
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2010-09-07 14:54:50 -04:00
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2010-09-06 11:18:20 -04:00
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num_cps_dp, num_cps_l = ctx.mod.get_global('g_num_cps')
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2010-09-07 14:54:50 -04:00
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cuda.memset_d32(num_cps_dp, num_cps, 1)
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2010-09-09 11:36:14 -04:00
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# TODO: "if debug >= 3"
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print "Uploaded stream to card:"
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CPDataStream.print_record(ctx, cp_stream, 5)
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2010-09-06 11:18:20 -04:00
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self.cps_uploaded = True
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2010-09-10 14:43:20 -04:00
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def call_setup(self, ctx):
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2010-09-06 11:18:20 -04:00
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if not self.cps_uploaded:
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raise Error("Cannot call IterThread before uploading CPs")
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2010-09-07 14:54:50 -04:00
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num_cps_st_dp, num_cps_st_l = ctx.mod.get_global('g_num_cps_started')
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cuda.memset_d32(num_cps_st_dp, 0, 1)
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2010-09-10 14:43:20 -04:00
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def _call(self, ctx, func):
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# Get texture reference from the Palette
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# TODO: more elegant method than reaching into ctx.ptx?
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2010-09-09 11:36:14 -04:00
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tr = ctx.ptx.instances[PaletteLookup].texref
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2010-09-10 14:43:20 -04:00
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super(IterThread, self)._call(ctx, func, texrefs=[tr])
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2010-09-03 00:52:27 -04:00
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2010-09-10 14:43:20 -04:00
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def call_teardown(self, ctx):
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2010-09-11 13:15:36 -04:00
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def print_thing(s, a):
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print '%s:' % s
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for i, r in enumerate(a):
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2010-09-12 13:45:55 -04:00
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for j in range(0,len(r),ctx.warps_per_cta):
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2010-09-12 11:09:47 -04:00
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print '%2d' % i,
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2010-09-12 13:45:55 -04:00
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for k in range(j,j+ctx.warps_per_cta,8):
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2010-09-12 11:09:47 -04:00
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print '\t' + ' '.join(
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['%8g'%np.mean(r[l]) for l in range(k,k+8)])
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2010-09-11 13:15:36 -04:00
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2010-09-12 13:45:55 -04:00
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rounds = ctx.get_per_thread('g_num_rounds', np.int32, shaped=True)
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writes = ctx.get_per_thread('g_num_writes', np.int32, shaped=True)
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2010-09-11 13:15:36 -04:00
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print_thing("Rounds", rounds)
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print_thing("Writes", writes)
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2010-09-12 13:45:55 -04:00
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print "Total number of rounds:", np.sum(rounds)
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2010-09-11 13:15:36 -04:00
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dp, l = ctx.mod.get_global('g_num_cps_started')
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cps_started = cuda.from_device(dp, 1, np.uint32)
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print "CPs started:", cps_started
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2010-09-09 11:36:14 -04:00
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2010-10-07 11:21:43 -04:00
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class CameraTransform(object):
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2010-09-09 11:36:14 -04:00
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shortname = 'camera'
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def deps(self):
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return [CPDataStream]
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def rotate(self, rotated_x, rotated_y, x, y):
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"""
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Rotate an IFS-space coordinate as defined by the camera.
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"""
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if features.camera_rotation:
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assert rotated_x.name != x.name and rotated_y.name != y.name
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with block("Rotate %s, %s to camera alignment" % (x, y)):
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reg.f32('rot_center_x rot_center_y')
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cp.get_v2(cpA, rot_center_x, 'cp.rot_center[0]',
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rot_center_y, 'cp.rot_center[1]')
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op.sub.f32(x, x, rot_center_x)
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op.sub.f32(y, y, rot_center_y)
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reg.f32('rot_sin_t rot_cos_t rot_old_x rot_old_y')
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cp.get_v2(cpA, rot_cos_t, 'cos(cp.rotate * 2 * pi / 360.)',
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rot_sin_t, '-sin(cp.rotate * 2 * pi / 360.)')
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comment('rotated_x = x * cos(t) - y * sin(t) + rot_center_x')
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op.fma.rn.f32(rotated_x, x, rot_cos_t, rot_center_x)
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op.fma.rn.f32(rotated_x, y, rot_sin_t, rotated_x)
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op.neg.f32(rot_sin_t, rot_sin_t)
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comment('rotated_y = x * sin(t) + y * cos(t) + rot_center_y')
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op.fma.rn.f32(rotated_y, x, rot_sin_t, rot_center_y)
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op.fma.rn.f32(rotated_y, y, rot_cos_t, rotated_y)
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# TODO: if this is a register-critical section, reloading
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# rot_center_[xy] here should save two regs. OTOH, if this is
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# *not* reg-crit, moving the subtraction above to new variables
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# may save a few clocks
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op.add.f32(x, x, rot_center_x)
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op.add.f32(y, y, rot_center_y)
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else:
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comment("No camera rotation in this kernel")
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op.mov.f32(rotated_x, x)
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op.mov.f32(rotated_y, y)
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def get_norm(self, norm_x, norm_y, x, y):
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"""
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Find the [0,1]-normalized floating-point histogram coordinates
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``norm_x, norm_y`` from the given IFS-space coordinates ``x, y``.
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"""
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self.rotate(norm_x, norm_y, x, y)
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with block("Scale rotated points to [0,1]-normalized coordinates"):
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reg.f32('cam_scale cam_offset')
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cp.get_v2(cpA, cam_scale, 'cp.camera.norm_scale[0]',
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cam_offset, 'cp.camera.norm_offset[0]')
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op.fma.f32(norm_x, norm_x, cam_scale, cam_offset)
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cp.get_v2(cpA, cam_scale, 'cp.camera.norm_scale[1]',
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cam_offset, 'cp.camera.norm_offset[1]')
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op.fma.f32(norm_y, norm_y, cam_scale, cam_offset)
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def get_index(self, index, x, y, pred=None):
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"""
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Find the histogram index (as a u32) from the IFS spatial coordinate in
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``x, y``.
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If the coordinates are out of bounds, 0xffffffff will be stored to
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``index``. If ``pred`` is given, it will be set if the point is valid,
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and cleared if not.
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"""
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# A few instructions could probably be shaved off of this one
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with block("Find histogram index"):
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reg.f32('norm_x norm_y')
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self.rotate(norm_x, norm_y, x, y)
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comment('Scale and offset from IFS to index coordinates')
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reg.f32('cam_scale cam_offset')
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cp.get_v2(cpA, cam_scale, 'cp.camera.idx_scale[0]',
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cam_offset, 'cp.camera.idx_offset[0]')
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op.fma.rn.f32(norm_x, norm_x, cam_scale, cam_offset)
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cp.get_v2(cpA, cam_scale, 'cp.camera.idx_scale[1]',
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cam_offset, 'cp.camera.idx_offset[1]')
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op.fma.rn.f32(norm_y, norm_y, cam_scale, cam_offset)
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comment('Check for bad value')
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reg.u32('index_x index_y')
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if not pred:
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pred = reg.pred('p_valid')
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op.cvt.rzi.s32.f32(index_x, norm_x)
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op.setp.ge.s32(pred, index_x, 0)
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op.setp.lt.and_.s32(pred, index_x, features.hist_width, pred)
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op.cvt.rzi.s32.f32(index_y, norm_y)
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op.setp.ge.and_.s32(pred, index_y, 0, pred)
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op.setp.lt.and_.s32(pred, index_y, features.hist_height, pred)
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op.mad.lo.u32(index, index_y, features.hist_stride, index_x)
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op.mov.u32(index, 0xffffffff, ifnotp=pred)
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|
2010-10-07 11:21:43 -04:00
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class PaletteLookup(object):
|
2010-09-09 11:36:14 -04:00
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shortname = "palette"
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# Resolution of texture on device. Bigger = more palette rez, maybe slower
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texheight = 16
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def __init__(self):
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self.texref = None
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|
def deps(self):
|
|
|
|
return [CPDataStream]
|
|
|
|
|
|
|
|
def module_setup(self):
|
|
|
|
mem.global_.texref('t_palette')
|
|
|
|
|
2010-09-12 23:45:38 -04:00
|
|
|
def look_up(self, r, g, b, a, color, norm_time, ifp):
|
2010-09-09 11:36:14 -04:00
|
|
|
"""
|
|
|
|
Look up the values of ``r, g, b, a`` corresponding to ``color_coord``
|
|
|
|
at the CP indexed in ``timestamp_idx``. Note that both ``color_coord``
|
|
|
|
and ``timestamp_idx`` should be [0,1]-normalized floats.
|
|
|
|
"""
|
|
|
|
op.tex._2d.v4.f32.f32(vec(r, g, b, a),
|
2010-09-12 23:45:38 -04:00
|
|
|
addr([t_palette, ', ', vec(norm_time, color)]), ifp=ifp)
|
2010-09-09 11:36:14 -04:00
|
|
|
if features.non_box_temporal_filter:
|
|
|
|
raise NotImplementedError("Non-box temporal filters not supported")
|
|
|
|
|
|
|
|
def upload_palette(self, ctx, frame, cp_list):
|
|
|
|
"""
|
|
|
|
Extract the palette from the given list of interpolated CPs, and upload
|
|
|
|
it to the device as a texture.
|
|
|
|
"""
|
|
|
|
# TODO: figure out if storing the full list is an actual drag on
|
|
|
|
# performance/memory
|
|
|
|
if frame.center_cp.temporal_filter_type != 0:
|
|
|
|
# TODO: make texture sample based on time, not on CP index
|
|
|
|
raise NotImplementedError("Use box temporal filters for now")
|
|
|
|
pal = np.ndarray((self.texheight, 256, 4), dtype=np.float32)
|
|
|
|
inv = float(len(cp_list) - 1) / (self.texheight - 1)
|
|
|
|
for y in range(self.texheight):
|
|
|
|
for x in range(256):
|
|
|
|
for c in range(4):
|
|
|
|
# TODO: interpolate here?
|
|
|
|
cy = int(round(y * inv))
|
|
|
|
pal[y][x][c] = cp_list[cy].palette.entries[x].color[c]
|
|
|
|
dev_array = cuda.make_multichannel_2d_array(pal, "C")
|
|
|
|
self.texref = ctx.mod.get_texref('t_palette')
|
|
|
|
# TODO: float16? or can we still use interp with int storage?
|
|
|
|
self.texref.set_format(cuda.array_format.FLOAT, 4)
|
|
|
|
self.texref.set_flags(cuda.TRSF_NORMALIZED_COORDINATES)
|
|
|
|
self.texref.set_filter_mode(cuda.filter_mode.LINEAR)
|
|
|
|
self.texref.set_address_mode(0, cuda.address_mode.CLAMP)
|
|
|
|
self.texref.set_address_mode(1, cuda.address_mode.CLAMP)
|
|
|
|
self.texref.set_array(dev_array)
|
2010-09-12 18:42:52 -04:00
|
|
|
self.pal = pal
|
2010-09-09 11:36:14 -04:00
|
|
|
|
2010-09-10 14:43:20 -04:00
|
|
|
def call_setup(self, ctx):
|
2010-09-09 11:36:14 -04:00
|
|
|
assert self.texref, "Must upload palette texture before launch!"
|
|
|
|
|
2010-10-07 11:21:43 -04:00
|
|
|
class HistScatter(object):
|
2010-09-09 11:36:14 -04:00
|
|
|
shortname = "hist"
|
|
|
|
def deps(self):
|
|
|
|
return [CPDataStream, CameraTransform, PaletteLookup]
|
|
|
|
|
|
|
|
def module_setup(self):
|
|
|
|
mem.global_.f32('g_hist_bins',
|
|
|
|
features.hist_height * features.hist_stride * 4)
|
2010-09-12 01:09:04 -04:00
|
|
|
comment("Target to ensure fake local values get written")
|
|
|
|
mem.global_.f32('g_hist_dummy')
|
2010-09-09 11:36:14 -04:00
|
|
|
|
|
|
|
def entry_setup(self):
|
2010-09-12 01:09:04 -04:00
|
|
|
comment("Fake bins for fake scatter")
|
|
|
|
mem.local.f32('l_scatter_fake_adr')
|
|
|
|
mem.local.f32('l_scatter_fake_alpha')
|
|
|
|
|
|
|
|
def entry_teardown(self):
|
|
|
|
with block("Store fake histogram bins to dummy global"):
|
|
|
|
reg.b32('hist_dummy')
|
|
|
|
op.ld.local.b32(hist_dummy, addr(l_scatter_fake_adr))
|
|
|
|
op.st.volatile.b32(addr(g_hist_dummy), hist_dummy)
|
|
|
|
op.ld.local.b32(hist_dummy, addr(l_scatter_fake_alpha))
|
|
|
|
op.st.volatile.b32(addr(g_hist_dummy), hist_dummy)
|
2010-09-09 11:36:14 -04:00
|
|
|
|
2010-09-12 23:45:38 -04:00
|
|
|
def scatter(self, hist_index, color, xf_idx, p_valid, type='ldst'):
|
2010-09-09 11:36:14 -04:00
|
|
|
"""
|
|
|
|
Scatter the given point directly to the histogram bins. I think this
|
|
|
|
technique has the worst performance of all of 'em. Accesses ``cpA``
|
|
|
|
directly.
|
|
|
|
"""
|
|
|
|
with block("Scatter directly to buffer"):
|
|
|
|
reg.u32('hist_bin_addr')
|
|
|
|
op.mov.u32(hist_bin_addr, g_hist_bins)
|
|
|
|
op.mad.lo.u32(hist_bin_addr, hist_index, 16, hist_bin_addr)
|
|
|
|
|
2010-09-12 02:01:03 -04:00
|
|
|
if type == 'fake_notex':
|
|
|
|
op.st.local.u32(addr(l_scatter_fake_adr), hist_bin_addr)
|
|
|
|
op.st.local.f32(addr(l_scatter_fake_alpha), color)
|
|
|
|
return
|
|
|
|
|
2010-09-09 11:36:14 -04:00
|
|
|
reg.f32('r g b a norm_time')
|
|
|
|
cp.get(cpA, norm_time, 'cp.norm_time')
|
2010-09-12 23:45:38 -04:00
|
|
|
palette.look_up(r, g, b, a, color, norm_time, ifp=p_valid)
|
2010-09-09 11:36:14 -04:00
|
|
|
# TODO: look up, scale by xform visibility
|
2010-09-11 13:15:36 -04:00
|
|
|
# TODO: Make this more performant
|
2010-09-12 01:09:04 -04:00
|
|
|
if type == 'ldst':
|
|
|
|
reg.f32('gr gg gb ga')
|
2010-09-12 23:45:38 -04:00
|
|
|
op.ld.v4.f32(vec(gr, gg, gb, ga), addr(hist_bin_addr),
|
|
|
|
ifp=p_valid)
|
2010-09-12 01:09:04 -04:00
|
|
|
op.add.f32(gr, gr, r)
|
|
|
|
op.add.f32(gg, gg, g)
|
|
|
|
op.add.f32(gb, gb, b)
|
|
|
|
op.add.f32(ga, ga, a)
|
2010-09-12 23:45:38 -04:00
|
|
|
op.st.v4.f32(addr(hist_bin_addr), vec(gr, gg, gb, ga),
|
|
|
|
ifp=p_valid)
|
2010-09-12 01:09:04 -04:00
|
|
|
elif type == 'red':
|
|
|
|
for i, val in enumerate([r, g, b, a]):
|
2010-09-12 23:45:38 -04:00
|
|
|
op.red.add.f32(addr(hist_bin_addr,4*i), val, ifp=p_valid)
|
2010-09-12 01:09:04 -04:00
|
|
|
elif type == 'fake':
|
|
|
|
op.st.local.u32(addr(l_scatter_fake_adr), hist_bin_addr)
|
|
|
|
op.st.local.f32(addr(l_scatter_fake_alpha), a)
|
2010-09-09 11:36:14 -04:00
|
|
|
|
2010-09-10 14:43:20 -04:00
|
|
|
def call_setup(self, ctx):
|
2010-09-09 11:36:14 -04:00
|
|
|
hist_bins_dp, hist_bins_l = ctx.mod.get_global('g_hist_bins')
|
|
|
|
cuda.memset_d32(hist_bins_dp, 0, hist_bins_l/4)
|
|
|
|
|
|
|
|
def get_bins(self, ctx, features):
|
|
|
|
hist_bins_dp, hist_bins_l = ctx.mod.get_global('g_hist_bins')
|
|
|
|
return cuda.from_device(hist_bins_dp,
|
|
|
|
(features.hist_height, features.hist_stride, 4),
|
|
|
|
dtype=np.float32)
|
2010-09-03 00:52:27 -04:00
|
|
|
|
2010-09-11 13:15:36 -04:00
|
|
|
|
2010-10-07 11:21:43 -04:00
|
|
|
class ShufflePoints(object):
|
2010-09-12 00:17:18 -04:00
|
|
|
"""
|
|
|
|
Shuffle points in shared memory. See helpers/shuf.py for details.
|
|
|
|
"""
|
|
|
|
shortname = "shuf"
|
|
|
|
|
|
|
|
def module_setup(self):
|
|
|
|
# TODO: if needed, merge this shared memory block with others
|
|
|
|
mem.shared.f32('s_shuf_data', ctx.threads_per_cta)
|
|
|
|
|
|
|
|
def shuffle(self, *args, **kwargs):
|
|
|
|
"""
|
|
|
|
Shuffle the data from each register in args across threads. Keyword
|
2010-09-12 11:09:47 -04:00
|
|
|
argument ``bar`` specifies which barrier to use (default is 2).
|
2010-09-12 00:17:18 -04:00
|
|
|
"""
|
2010-09-12 11:09:47 -04:00
|
|
|
bar = kwargs.pop('bar', 2)
|
2010-09-12 00:17:18 -04:00
|
|
|
with block("Shuffle across threads"):
|
|
|
|
reg.u32('shuf_read shuf_write')
|
|
|
|
with block("Calculate read and write offsets"):
|
|
|
|
reg.u32('shuf_off shuf_laneid')
|
|
|
|
op.mov.u32(shuf_off, '%tid.x')
|
|
|
|
op.mov.u32(shuf_write, s_shuf_data)
|
|
|
|
op.mad.lo.u32(shuf_write, shuf_off, 4, shuf_write)
|
|
|
|
op.mov.u32(shuf_laneid, '%laneid')
|
|
|
|
op.mad.lo.u32(shuf_off, shuf_laneid, 32, shuf_off)
|
|
|
|
op.and_.b32(shuf_off, shuf_off, ctx.threads_per_cta - 1)
|
|
|
|
op.mov.u32(shuf_read, s_shuf_data)
|
|
|
|
op.mad.lo.u32(shuf_read, shuf_off, 4, shuf_read)
|
|
|
|
for var in args:
|
|
|
|
op.bar.sync(bar)
|
2010-09-13 00:20:15 -04:00
|
|
|
op.st.volatile.shared.b32(addr(shuf_write), var)
|
2010-09-12 00:17:18 -04:00
|
|
|
op.bar.sync(bar)
|
2010-09-13 00:20:15 -04:00
|
|
|
op.ld.volatile.shared.b32(var, addr(shuf_read))
|
2010-09-11 13:15:36 -04:00
|
|
|
|
2010-10-07 11:21:43 -04:00
|
|
|
|
2010-10-01 01:20:20 -04:00
|
|
|
class MWCRNG(object):
|
2010-10-07 11:21:43 -04:00
|
|
|
"""
|
|
|
|
Marsaglia multiply-with-carry random number generator. Produces very long
|
|
|
|
periods with sufficient statistical properties using only three 32-bit
|
|
|
|
state registers. Since each thread uses a separate multiplier, no two
|
|
|
|
threads will ever be on the same sequence, but beyond this the independence
|
|
|
|
of each thread's sequence was not explicitly tested.
|
|
|
|
|
|
|
|
The RNG must be seeded at least once per entry point using the ``seed``
|
|
|
|
method.
|
|
|
|
"""
|
|
|
|
def __init__(self, entry):
|
2010-10-01 01:20:20 -04:00
|
|
|
# TODO: install this in data directory or something
|
2010-08-28 16:56:05 -04:00
|
|
|
if not os.path.isfile('primes.bin'):
|
|
|
|
raise EnvironmentError('primes.bin not found')
|
2010-10-07 11:21:43 -04:00
|
|
|
self.nthreads_ready = 0
|
2010-10-01 01:20:20 -04:00
|
|
|
self.mults, self.state = None, None
|
2010-10-09 11:18:58 -04:00
|
|
|
self.entry = entry
|
2010-10-01 01:20:20 -04:00
|
|
|
|
2010-10-09 11:18:58 -04:00
|
|
|
entry.add_ptr_param('u32', 'mwc_mults')
|
|
|
|
entry.add_ptr_param('u32', 'mwc_states')
|
2010-10-07 11:21:43 -04:00
|
|
|
|
|
|
|
with entry.head():
|
2010-10-09 11:18:58 -04:00
|
|
|
self.entry_head()
|
|
|
|
entry.tail_callback(self.entry_tail)
|
2010-10-07 11:21:43 -04:00
|
|
|
|
2010-10-09 11:18:58 -04:00
|
|
|
def entry_head(self):
|
|
|
|
e, r, o, m, p, s = self.entry.locals
|
2010-10-07 11:21:43 -04:00
|
|
|
gtid = s.ctaid_x * s.ntid_x + s.tid_x
|
|
|
|
r.mwc_mult, r.mwc_state, r.mwc_carry = r.u32(), r.u32(), r.u32()
|
|
|
|
r.mwc_mult = o.ld(p.mwc_mults[gtid])
|
|
|
|
r.mwc_state, r.mwc_carry = o.ld.v2(p.mwc_states[2*gtid])
|
|
|
|
|
2010-10-09 11:18:58 -04:00
|
|
|
def entry_tail(self):
|
|
|
|
e, r, o, m, p, s = self.entry.locals
|
2010-10-07 11:21:43 -04:00
|
|
|
gtid = s.ctaid_x * s.ntid_x + s.tid_x
|
|
|
|
o.st.v2.u32(p.mwc_states[2*gtid], r.mwc_state, r.mwc_carry)
|
|
|
|
|
2010-10-09 11:18:58 -04:00
|
|
|
def next_b32(self):
|
|
|
|
e, r, o, m, p, s = self.entry.locals
|
2010-10-07 11:21:43 -04:00
|
|
|
carry = o.cvt.u64(r.mwc_carry)
|
|
|
|
mwc_out = o.mad.wide(r.mwc_mult, r.mwc_state, carry)
|
|
|
|
r.mwc_state, r.mwc_carry = o.split.v2(mwc_out)
|
2010-10-01 01:20:20 -04:00
|
|
|
return r.mwc_state
|
|
|
|
|
2010-10-09 11:18:58 -04:00
|
|
|
def next_f32_01(self):
|
|
|
|
e, r, o, m, p, s = self.entry.locals
|
2010-10-01 01:20:20 -04:00
|
|
|
mwc_float = o.cvt.rn.f32.u32(self.next_b32())
|
|
|
|
return o.mul.f32(mwc_float, 1./(1<<32))
|
|
|
|
|
2010-10-09 11:18:58 -04:00
|
|
|
def next_f32_11(self):
|
|
|
|
e, r, o, m, p, s = self.entry.locals
|
2010-10-01 01:20:20 -04:00
|
|
|
mwc_float = o.cvt.rn.f32.s32(self.next_b32())
|
|
|
|
return o.mul.f32(mwc_float, 1./(1<<31))
|
|
|
|
|
2010-10-07 11:21:43 -04:00
|
|
|
def seed(self, ctx, seed=None, force=False):
|
2010-09-10 14:43:20 -04:00
|
|
|
"""
|
|
|
|
Seed the random number generators with values taken from a
|
|
|
|
``np.random`` instance.
|
|
|
|
"""
|
2010-10-01 01:20:20 -04:00
|
|
|
if force or self.nthreads_ready < ctx.nthreads:
|
2010-10-07 11:21:43 -04:00
|
|
|
if seed:
|
|
|
|
rand = np.random.RandomState(seed)
|
|
|
|
else:
|
|
|
|
rand = np.random
|
2010-10-01 01:20:20 -04:00
|
|
|
# Load raw big-endian u32 multipliers from primes.bin.
|
|
|
|
with open('primes.bin') as primefp:
|
|
|
|
dt = np.dtype(np.uint32).newbyteorder('B')
|
|
|
|
mults = np.frombuffer(primefp.read(), dtype=dt)
|
|
|
|
# Randomness in choosing multipliers is good, but larger multipliers
|
|
|
|
# have longer periods, which is also good. This is a compromise.
|
|
|
|
mults = np.array(mults[:ctx.nthreads*4])
|
|
|
|
rand.shuffle(mults)
|
2010-10-07 11:21:43 -04:00
|
|
|
#locked_mults = ctx.hostpool.allocate(ctx.nthreads, np.uint32)
|
|
|
|
#locked_mults[:] = mults[ctx.nthreads]
|
|
|
|
#self.mults = ctx.pool.allocate(4*ctx.nthreads)
|
|
|
|
#cuda.memcpy_htod_async(self.mults, locked_mults.base, ctx.stream)
|
|
|
|
self.mults = cuda.mem_alloc(4*ctx.nthreads)
|
|
|
|
cuda.memcpy_htod(self.mults, mults[:ctx.nthreads].tostring())
|
2010-10-01 01:20:20 -04:00
|
|
|
# Intentionally excludes both 0 and (2^32-1), as they can lead to
|
|
|
|
# degenerate sequences of period 0
|
|
|
|
states = np.array(rand.randint(1, 0xffffffff, size=2*ctx.nthreads),
|
|
|
|
dtype=np.uint32)
|
2010-10-07 11:21:43 -04:00
|
|
|
#locked_states = ctx.hostpool.allocate(2*ctx.nthreads, np.uint32)
|
|
|
|
#locked_states[:] = states
|
|
|
|
#self.states = ctx.pool.allocate(8*ctx.nthreads)
|
|
|
|
#cuda.memcpy_htod_async(self.states, locked_states, ctx.stream)
|
|
|
|
self.states = cuda.mem_alloc(8*ctx.nthreads)
|
|
|
|
cuda.memcpy_htod(self.states, states.tostring())
|
2010-10-01 01:20:20 -04:00
|
|
|
self.nthreads_ready = ctx.nthreads
|
|
|
|
ctx.set_param('mwc_mults', self.mults)
|
|
|
|
ctx.set_param('mwc_states', self.states)
|
|
|
|
|
2010-10-07 11:21:43 -04:00
|
|
|
class MWCRNGTest(object):
|
|
|
|
"""
|
|
|
|
Test the ``MWCRNG`` class. This is not a test of the generator's
|
|
|
|
statistical properties, but merely a test that the generator is implemented
|
|
|
|
correctly on the GPU.
|
|
|
|
"""
|
2010-09-01 22:46:55 -04:00
|
|
|
rounds = 5000
|
2010-09-01 21:09:40 -04:00
|
|
|
|
2010-10-01 01:20:20 -04:00
|
|
|
def __init__(self, entry):
|
|
|
|
self.mwc = MWCRNG(entry)
|
2010-10-09 11:18:58 -04:00
|
|
|
entry.add_ptr_param('u64', 'mwc_test_sums')
|
2010-10-01 01:20:20 -04:00
|
|
|
|
|
|
|
with entry.body():
|
2010-10-07 11:21:43 -04:00
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self.entry_body(entry)
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|
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|
def entry_body(self, entry):
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|
|
e, r, o, m, p, s = entry.locals
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r.sum = r.u64(0)
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r.count = r.f32(self.rounds)
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|
start = e.label()
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r.sum = r.sum + o.cvt.u64.u32(self.mwc.next_b32(e))
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r.count = r.count - 1
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with r.count > 0:
|
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o.bra.uni(start)
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e.comment('yay')
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gtid = s.ctaid_x * s.ntid_x + s.tid_x
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o.st(p.mwc_test_sums[gtid], r.sum)
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def run_test(self, ctx):
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self.mwc.seed(ctx)
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mults = cuda.from_device(self.mwc.mults, ctx.nthreads, np.uint32)
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states = cuda.from_device(self.mwc.states, ctx.nthreads, np.uint64)
|
2010-10-01 01:20:20 -04:00
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for trial in range(2):
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print "Trial %d, on CPU: " % trial,
|
2010-10-07 11:21:43 -04:00
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sums = np.zeros_like(states)
|
2010-10-01 01:20:20 -04:00
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ctime = time.time()
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|
|
for i in range(self.rounds):
|
2010-10-07 11:21:43 -04:00
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vals = states & 0xffffffff
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carries = states >> 32
|
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|
states = mults * vals + carries
|
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|
sums += states & 0xffffffff
|
2010-10-01 01:20:20 -04:00
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|
ctime = time.time() - ctime
|
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|
|
print "Took %g seconds." % ctime
|
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|
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|
|
|
|
print "Trial %d, on device: " % trial,
|
2010-10-07 11:21:43 -04:00
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|
dsums = cuda.mem_alloc(8*ctx.nthreads)
|
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|
|
ctx.set_param('mwc_test_sums', dsums)
|
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|
print "Took %g seconds." % ctx.call_timed()
|
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|
|
dsums = cuda.from_device(dsums, ctx.nthreads, np.uint64)
|
2010-10-01 01:20:20 -04:00
|
|
|
if not np.all(np.equal(sums, dsums)):
|
|
|
|
print "Sum discrepancy!"
|
|
|
|
print sums
|
|
|
|
print dsums
|
2010-08-30 14:45:44 -04:00
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|
2010-10-07 11:21:43 -04:00
|
|
|
class MWCRNGFloatsTest(object):
|
2010-09-10 19:36:39 -04:00
|
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|
"""
|
|
|
|
Note this only tests that the distributions are in the correct range, *not*
|
|
|
|
that they have good random properties. MWC is a suitable algorithm, but
|
|
|
|
implementation bugs may still lead to poor performance.
|
|
|
|
"""
|
|
|
|
rounds = 1024
|
|
|
|
entry_name = 'MWC_RNG_floats_test'
|
|
|
|
|
|
|
|
def deps(self):
|
|
|
|
return [MWCRNG]
|
|
|
|
|
|
|
|
def module_setup(self):
|
2010-09-12 11:13:53 -04:00
|
|
|
mem.global_.f32('mwc_rng_float_01_test_sums', ctx.nthreads)
|
|
|
|
mem.global_.f32('mwc_rng_float_01_test_mins', ctx.nthreads)
|
|
|
|
mem.global_.f32('mwc_rng_float_01_test_maxs', ctx.nthreads)
|
|
|
|
mem.global_.f32('mwc_rng_float_11_test_sums', ctx.nthreads)
|
|
|
|
mem.global_.f32('mwc_rng_float_11_test_mins', ctx.nthreads)
|
|
|
|
mem.global_.f32('mwc_rng_float_11_test_maxs', ctx.nthreads)
|
2010-09-10 19:36:39 -04:00
|
|
|
|
|
|
|
def loop(self, kind):
|
|
|
|
with block('Sum %d floats in %s' % (self.rounds, kind)):
|
2010-09-11 00:12:02 -04:00
|
|
|
reg.f32('loopct val rsum rmin rmax')
|
2010-09-10 19:36:39 -04:00
|
|
|
reg.pred('p_done')
|
|
|
|
op.mov.f32(loopct, 0.)
|
2010-09-11 00:12:02 -04:00
|
|
|
op.mov.f32(rsum, 0.)
|
2010-09-10 19:36:39 -04:00
|
|
|
op.mov.f32(rmin, 2.)
|
|
|
|
op.mov.f32(rmax, -2.)
|
|
|
|
label('loopstart' + kind)
|
|
|
|
getattr(mwc, 'next_f32_' + kind)(val)
|
2010-09-11 00:12:02 -04:00
|
|
|
op.add.f32(rsum, rsum, val)
|
2010-09-10 19:36:39 -04:00
|
|
|
op.min.f32(rmin, rmin, val)
|
|
|
|
op.max.f32(rmax, rmax, val)
|
|
|
|
op.add.f32(loopct, loopct, 1.)
|
|
|
|
op.setp.ge.f32(p_done, loopct, float(self.rounds))
|
|
|
|
op.bra('loopstart' + kind, ifnotp=p_done)
|
2010-09-11 00:12:02 -04:00
|
|
|
op.mul.f32(rsum, rsum, 1./self.rounds)
|
|
|
|
std.store_per_thread('mwc_rng_float_%s_test_sums' % kind, rsum,
|
2010-09-10 19:36:39 -04:00
|
|
|
'mwc_rng_float_%s_test_mins' % kind, rmin,
|
|
|
|
'mwc_rng_float_%s_test_maxs' % kind, rmax)
|
|
|
|
|
|
|
|
def entry(self):
|
|
|
|
self.loop('01')
|
|
|
|
self.loop('11')
|
|
|
|
|
|
|
|
def call_teardown(self, ctx):
|
|
|
|
# Tolerance of all-threads averages
|
|
|
|
tol = 0.05
|
|
|
|
# float distribution kind, test kind, expected value, limit func
|
|
|
|
tests = [
|
|
|
|
('01', 'sums', 0.5, None),
|
|
|
|
('01', 'mins', 0.0, np.min),
|
|
|
|
('01', 'maxs', 1.0, np.max),
|
|
|
|
('11', 'sums', 0.0, None),
|
|
|
|
('11', 'mins', -1.0, np.min),
|
|
|
|
('11', 'maxs', 1.0, np.max)
|
|
|
|
]
|
|
|
|
|
|
|
|
for fkind, rkind, exp, lim in tests:
|
2010-09-12 13:45:55 -04:00
|
|
|
name = 'mwc_rng_float_%s_test_%s' % (fkind, rkind)
|
|
|
|
vals = ctx.get_per_thread(name, np.float32)
|
2010-09-10 19:36:39 -04:00
|
|
|
avg = np.mean(vals)
|
|
|
|
if np.abs(avg - exp) > tol:
|
|
|
|
raise PTXTestFailure("%s %s %g too far from %g" %
|
|
|
|
(fkind, rkind, avg, exp))
|
|
|
|
if lim is None: continue
|
|
|
|
if lim([lim(vals), exp]) != exp:
|
|
|
|
raise PTXTestFailure("%s %s %g violates hard limit %g" %
|
|
|
|
(fkind, rkind, lim(vals), exp))
|
2010-09-10 18:01:50 -04:00
|
|
|
|
2010-10-07 11:21:43 -04:00
|
|
|
class CPDataStream(object):
|
2010-09-03 00:52:27 -04:00
|
|
|
"""DataStream which stores the control points."""
|
2010-09-06 16:09:37 -04:00
|
|
|
shortname = 'cp'
|
2010-08-28 16:56:05 -04:00
|
|
|
|
2010-10-07 11:21:43 -04:00
|
|
|
class Timeouter(object):
|
2010-09-11 13:18:40 -04:00
|
|
|
"""Time-out infinite loops so that data can still be retrieved."""
|
|
|
|
shortname = 'timeout'
|
|
|
|
|
|
|
|
def entry_setup(self):
|
|
|
|
mem.shared.u64('s_timeouter_start_time')
|
|
|
|
with block("Load start time for this block"):
|
|
|
|
reg.u64('now')
|
|
|
|
op.mov.u64(now, '%clock64')
|
|
|
|
op.st.shared.u64(addr(s_timeouter_start_time), now)
|
|
|
|
|
|
|
|
def check_time(self, secs):
|
|
|
|
"""
|
|
|
|
Drop this into your mainloop somewhere.
|
|
|
|
"""
|
|
|
|
# TODO: if debug.device_timeout_loops or whatever
|
|
|
|
with block("Check current time for this loop"):
|
|
|
|
d = cuda.Context.get_device()
|
|
|
|
clks = int(secs * d.clock_rate * 1000)
|
|
|
|
reg.u64('now then')
|
|
|
|
op.mov.u64(now, '%clock64')
|
|
|
|
op.ld.shared.u64(then, addr(s_timeouter_start_time))
|
|
|
|
op.sub.u64(now, now, then)
|
|
|
|
std.asrt("Loop timed out", 'lt.u64', now, clks)
|
|
|
|
|
|
|
|
|