lg.draw_frame(False)
pp = PdfPages('../plot/capacity_miss.pdf')
pp.savefig( bbox_inches='tight', dpi=300)
pp.close()
plt.close(fig)
# average offload
res_p, res_o = [], []
for ant in [2,4,6,8]:
res = utils.read_pickle('../dump/proc_offload_%d.pkl'%ant)
res_p.append(res[0][0])
res_o.append(res[1][0])
orig_proc = [741, 927, 1113, 1299]
fig = plt.figure(figsize=(4*1.7, 3*1.7))
ax = plt.gca()
N = 4
ind = np.arange(N)+0.2 # the x locations for the groups
width = 0.15
rectso = ax.bar(ind, orig_proc, width, color = 'white', alpha=1, capsize=8, linewidth= 1, label='Original Proc')
rects1 = ax.bar(ind+0.15, res_p, width, color = 'darkgray', alpha=1, capsize=8, linewidth= 1, label='Local Proc')
rects2 = ax.bar(ind+0.3, res_o, width, color = 'black', alpha=1, capsize=8, linewidth= 1, label='Offload')
for exp in exp_range:
for samples in samples_range:
for nants in nant_range:
for prior in prior_range:
for sched in sched_range:
# run the C code
args = ' -n %d -s 15000 -d %d -p 10 -S F -e O -D 0 -X %d -Y %d -Z %d'%(nants, dur, T_T, T_P, N_P)
print args
subprocess.check_call('../src/gd.o'+args, shell=True)
_,avg_proc,avg_offload = main(exp, samples, nants, nprocs, prior, sched, T_T, T_P, N_P)
for idx, ntrans in enumerate(range(nants)):
arrt = utils.read_pickle('../dump/gstat_exp%s_samp%d_trans%d_prior%d_sched%s_nant%d_nproc%d_T_T%d_T_P%d_N_P%d'%(exp, samples, idx, prior, sched,nants, nprocs, T_T, T_P, N_P))
arrps, arros = [], []
for idx, nproc in enumerate(range(nprocs)):
arrp = utils.read_pickle('../dump/gstat_exp%s_samp%d_proc%d_prior%d_sched%s_nant%d_nproc%d_T_T%d_T_P%d_N_P%d'%(exp, samples, idx, prior, sched,nants, nprocs, T_T, T_P, N_P))
arro = utils.read_pickle('../dump/gstat_exp%s_samp%d_offload%d_prior%d_sched%s_nant%d_nproc%d_T_T%d_T_P%d_N_P%d'%(exp, samples, idx, prior, sched,nants, nprocs, T_T, T_P, N_P))
# analyse the logs
res, arrp, arro, _, _ = time_analysis(arrp, arro)
arrts.append([arrt['duration']])
arrps.append(arrp)
sched_range = ['SCHED_FIFO']
nant_range = [4]
nprocs = 3
##############################
# slack cdf vs policy
exp = 'plain'
samples = 1000
prior = 99
sched_range = ['SCHED_FIFO', 'SCHED_RR', 'SCHED_OTHER']
nants = 4
stats = {}
for sched in sched_range:
obj = utils.read_pickle('../dump/gstat_exp%s_samp%d_prior%d_sched%s_nant%d_nproc%d'%(exp, samples, prior,sched,nants, nprocs))
stats[sched] = astat(obj.proc['slack'])
fig = plt.figure(figsize=(4*1.5, 3*1.5))
ax = plt.gca()
bins = np.linspace(-500, 2000, 1000)
plt.plot(bins, stats['SCHED_FIFO'].get_cdf(bins), linewidth=4, label='FIFO')
plt.plot(bins, stats['SCHED_RR'].get_cdf(bins), linewidth=4, label='RR', color='#999fff')
plt.plot(bins, stats['SCHED_OTHER'].get_cdf(bins), linewidth=4, label='OTHER', color='black')
# ax.set_yticks(np.arange(0,6,1))
plt.xlabel('Slack (us)')
plt.ylabel('CDF')
plt.ylim(0,1)
nospines(ax)
# for num_bss in num_bss_range:
for num_bss in [4]:
nprocs = int(num_bss*math.ceil(1.0*lmax/1000))
num_cores_bs = int(math.ceil(1.0*lmax/1000))
max_cores = int(math.ceil(1.0*lmax/1000))*num_bss
y1, y11 = [], []
for exp in exp_range:
y,z = [],[]
for rtt in rtt_range:
exp_str = 'exp%s_var%d_nbss%d_nants%d_ncores%d_Lmax%d_snr%d_mcs%d_delay%d'%(exp, var,num_bss, num_ants, max_cores, lmax, snr, mcs, rtt)
ress = utils.read_pickle('../dump/ress11_%s.pkl'%(exp_str))
# if ress[exp_str][0] < 0.0001:
# ress[exp_str][0] = 0
y.append(ress[exp_str][0])
z.append(ress[exp_str][2])
y1.append(y[:])
y11.append(z[:])
# global 16 cores
y,z = [],[]
for rtt in rtt_range:
exp_str = 'exp%s_var%d_nbss%d_nants%d_ncores%d_Lmax%d_snr%d_mcs%d_delay%d'%('global', var,num_bss, num_ants, 16, lmax, snr, mcs, rtt)
ress = utils.read_pickle('../dump/ress11_%s.pkl'%(exp_str))
y.append(ress[exp_str][0])
z.append(ress[exp_str][2])
