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- Committer: Suren A. Chilingaryan
- Date: 2018-09-19 03:32:56 UTC
- Revision ID: csa@suren.me-20180919033256-w9y50i624l5rehny
Benchmarks and figures for slides
- files added:
- analysis/phantom/README
- analysis/sino/phantom2176.py
- figs/slide-gen
- releases
- files modified:
- .bzrignore
added
removed
figs/slide-gen/algorithms.py
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#! /usr/bin/python
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import sys
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import numpy as np
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import matplotlib.pyplot as plt
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ops = 3526656000.
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#colors=['green', 'red']
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#colors=['#05500a', '#ff676c']
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colors=['#05500a', 'lightyellow']
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#colors2=['black', '#011268', '#15601a', '#ff676c', 'lightyellow']
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colors2=['#303030', '#2b83ba', 'limegreen', '#fdd5a5', '#d3ffd5', 'lightyellow']
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titles = np.genfromtxt('../bench/gpu.txt', dtype = 'S32', delimiter=",", usecols = [0])
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specs = np.genfromtxt('../bench/specs.txt', names = True, usecols = range(1,10))
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# Convert structured numpy array to standard ndarray
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specs = specs.view(np.float).reshape(specs.shape + (-1,))
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data = np.genfromtxt('../bench/tex.txt', names = True, usecols = range(1,10))
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# Convert structured numpy array to standard ndarray
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data = data.view(np.float).reshape(data.shape + (-1,))
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# Convert to GUPS
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tex_gups = np.vectorize(lambda v: ops / v / 1000 / 1000 if v > 0 else 0.)(data)
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data = np.genfromtxt('../bench/alu.txt', names = True, usecols = range(1,10))
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# Convert structured numpy array to standard ndarray
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alu_gups = data.view(np.float).reshape(data.shape + (-1,))
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#Extract
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std = np.round(tex_gups[0])
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tex_f1 = np.amax(tex_gups[0:12:4], axis=0)
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tex_f2 = np.amax(tex_gups[1:12:4], axis=0)
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tex_f4 = np.amax(tex_gups[2:12:4], axis=0)
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tex_h4 = np.amax(tex_gups[3:12:4], axis=0)
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#Standard
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lin_simple = tex_f1
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nn_simple = tex_f1
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# Compute maximum for precise and approximate modes
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plin_tex = np.amax([tex_f1, tex_f2], axis=0)
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alin_tex = plin_tex
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pnn_tex = np.amax([plin_tex, tex_f4], axis=0)
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ann_tex = np.amax([pnn_tex, tex_h4], axis=0)
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#Linear & Hybrid
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lin_f1 = np.amax(alu_gups[0:8:4], axis=0)
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lin_f2 = np.amax(alu_gups[1:8:4], axis=0)
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lin_f4 = np.amax(alu_gups[2:8:4], axis=0)
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lin_h4 = np.amax(alu_gups[3:8:4], axis=0)
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#Individual
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alu_fx = np.amax(alu_gups[0:3], axis=0)
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alu_xx = np.amax(alu_gups[0:4], axis=0)
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hyb_fx = np.amax(alu_gups[4:7], axis=0)
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hyb_xx = np.amax(alu_gups[4:8], axis=0)
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#oversampling & NN
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ovs_f1 = alu_gups[8];
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ovs_xx = np.amax(alu_gups[8:12], axis=0)
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nn_f1 = alu_gups[12];
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nn_fx = np.amax(alu_gups[12:15], axis=0)
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#nn_hx = np.amax(alu_gups[15], axis=0)
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nn_hx = alu_gups[15]
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# Compute maximum for precise and approximate modes
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plin = np.amax([lin_f1, lin_f2, lin_f4, plin_tex], axis=0)
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alin = np.amax([plin, lin_h4, ovs_xx, alin_tex], axis=0)
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pnn = np.amax([nn_fx, pnn_tex], axis=0)
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ann = np.amax([pnn, nn_hx, ann_tex], axis=0)
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# Single-slice mode
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plin_slice = np.amax([tex_f1, lin_f1], axis=0)
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alin_slice = np.amax([plin_slice, ovs_f1], axis=0)
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pnn_slice = np.amax([tex_f1, nn_f1], axis=0)
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ann_slice = pnn_slice
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# Compute speed-up
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#def speedup(x): return (x - std) / std
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def speedup(x): return x / std
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#print speedup(plin)
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#print speedup(alin)
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N = len(data[0])
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idx = np.arange(N)
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idx = np.fliplr([idx])[0]
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#print idx
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bar_width = 1.0
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bar_gap = 0.2
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group_gap = 0.6
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mult = (bar_width + bar_gap) + group_gap;
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fig, (p) = plt.subplots(1, 1)
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#p_atex = p.barh(mult * idx + 2 * (bar_width + bar_gap), speedup(atex), bar_width, color=colors[1], edgecolor='black')
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#p_ptex = p.barh(mult * idx + 2 * (bar_width + bar_gap), speedup(ptex), bar_width, color=colors[0], edgecolor='black')
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#p_ann = p.barh(mult * idx, speedup(ann), bar_width, hatch='\\\\', color=colors2[5], edgecolor='black')
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p_pnn = p.barh(mult * (idx + 0.35), speedup(pnn), bar_width, color=colors2[2], edgecolor='black')
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#p_alin = p.barh(mult * idx + bar_width + bar_gap, speedup(alin), bar_width, hatch="", color=colors2[4], edgecolor='black')
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p_plin = p.barh(mult * (idx + 0.35), speedup(plin), bar_width, hatch="", color=colors2[1], edgecolor='black')
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ax = plt.gca()
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ax.xaxis.grid(alpha=0.25)
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plt.xticks(np.arange(1,8.1,0.5))
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plt.yticks(mult * idx + (bar_width + bar_gap)/2, titles)
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plt.xlabel("speedup (times)")
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plt.xlim(1, 8.0)
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plt.ylim(0, 9*mult - group_gap)
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legend = p.legend(
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(p_plin[0], p_pnn[0]),
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("Linear", "Nearest-Neighbor"),
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loc='upper right')
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#, bbox_to_anchor=(0.5,-0.2), ncol=3)
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#plt.gca().add_artist(legend2)
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plt.savefig('algorithms.png', dpi=300, bbox_inches='tight', bbox_extra_artists=(legend,))
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plt.close()
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sys.exit()
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fig, (p) = plt.subplots(1, 1)
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#p_atex = p.barh(mult * idx + 2 * (bar_width + bar_gap), speedup(atex), bar_width, color=colors[1], edgecolor='black')
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#p_ptex = p.barh(mult * idx + 2 * (bar_width + bar_gap), speedup(ptex), bar_width, color=colors[0], edgecolor='black')
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p_alin = p.barh(mult * idx + bar_width + bar_gap, speedup(alin), bar_width, hatch="", color=colors[1], edgecolor='black')
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p_plin = p.barh(mult * idx + bar_width + bar_gap, speedup(plin), bar_width, hatch="", color=colors[0], edgecolor='black')
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p_ann = p.barh(mult * idx, speedup(ann), bar_width, hatch='\\\\', color=colors[1], edgecolor='black')
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p_pnn = p.barh(mult * idx, speedup(pnn), bar_width, hatch='\\\\', color=colors[0], edgecolor='black')
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ax = plt.gca()
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ax.xaxis.grid(alpha=0.25)
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plt.xticks(np.arange(1,8.1,0.5))
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plt.yticks(mult * idx + 2 * (bar_width + bar_gap)/2, titles)
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plt.xlabel("speedup (times)")
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plt.xlim(1, 8.0)
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plt.ylim(0, 9*mult - group_gap)
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legend = p.legend(
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(p_plin[0], p_pnn[0], p_alin[0], p_ann[0]),
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("Linear", "Nearest",
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"Linear & Oversampling / Half-float", "Nearest / Half-float"),
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loc='upper center', bbox_to_anchor=(0.5,-0.1), ncol=2)
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plt.savefig('speedup.pdf', dpi=300, bbox_inches='tight', bbox_extra_artists=(legend,))
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plt.close()
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fig, (p) = plt.subplots(1, 1)
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p_alin = p.barh(mult * idx + bar_width + bar_gap, speedup(alin_tex), bar_width, hatch="", color=colors[1], edgecolor='black')
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p_plin = p.barh(mult * idx + bar_width + bar_gap, speedup(plin_tex), bar_width, hatch="", color=colors[0], edgecolor='black')
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p_ann = p.barh(mult * idx, speedup(ann_tex), bar_width, hatch='\\\\', color=colors[1], edgecolor='black')
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p_pnn = p.barh(mult * idx, speedup(pnn_tex), bar_width, hatch='\\\\', color=colors[0], edgecolor='black')
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ax = plt.gca()
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ax.xaxis.grid(alpha=0.25)
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plt.xticks(np.arange(1,8.1,0.5))
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plt.yticks(mult * idx + 2 * (bar_width + bar_gap)/2, titles)
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plt.xlabel("speedup (times)")
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plt.xlim(1, 8.0)
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plt.ylim(0, 9*mult - group_gap)
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legend = p.legend(
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(p_plin[0], p_pnn[0], p_alin[0], p_ann[0]),
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("Linear", "Nearest",
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"Linear / Half-float", "Nearest / Half-float"),
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loc='upper center', bbox_to_anchor=(0.5,-0.1), ncol=2)
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plt.savefig('speedup_tex.pdf', dpi=300, bbox_inches='tight', bbox_extra_artists=(legend,))
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plt.close()
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fig, (p) = plt.subplots(1, 1)
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p_alin = p.barh(mult * idx + bar_width + bar_gap, speedup(alin_slice), bar_width, hatch="", color=colors[1], edgecolor='black')
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p_plin = p.barh(mult * idx + bar_width + bar_gap, speedup(plin_slice), bar_width, hatch="", color=colors[0], edgecolor='black')
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#p_ann = p.barh(mult * idx, speedup(ann_slice), bar_width, hatch='\\\\', color=colors[1], edgecolor='black')
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p_pnn = p.barh(mult * idx, speedup(pnn_slice), bar_width, hatch='\\\\', color=colors[0], edgecolor='black')
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ax = plt.gca()
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ax.xaxis.grid(alpha=0.25)
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plt.xticks(np.arange(1,8.1,0.5))
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plt.yticks(mult * idx + 2 * (bar_width + bar_gap)/2, titles)
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plt.xlabel("speedup (times)")
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plt.xlim(1, 8.0)
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plt.ylim(0, 9*mult - group_gap)
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legend = p.legend(
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(p_plin[0], p_pnn[0], p_alin[0], p_ann[0]),
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("Linear", "Nearest",
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"Oversampling"),
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loc='upper center', bbox_to_anchor=(0.5,-0.1), ncol=2)
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plt.savefig('speedup_slice.pdf', dpi=300, bbox_inches='tight', bbox_extra_artists=(legend,))
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plt.close()
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