def matrix_sweep_figs():
output_path = 'poster_figs'
if not os.path.exists(output_path):
os.makedirs(output_path)
block_dims = get_block_dims(250, 8000)
for block_dim in block_dims:
drawplots_fn(experiment_name_matrix_sweep, lambda x: x['job_completion_time'],
'job_completion_time', 'Job Completion Time [seconds]',
lambda x: x['tracefile'].find('syn_mat_mul_8_{}_combined'.format(block_dim)) >= 0,
'Global Schedulers - Synthetic Matrix Multiplication block dim {}'.format(block_dim),
'poster_figs/syn_matmult_global_8_{}.png'.format(block_dim))
def global_figs():
output_path = 'poster_figs'
if not os.path.exists(output_path):
os.makedirs(output_path)
for network_slowdown in [1, 10, 100, 1000]:
object_transfer_time_cost = .00000001 * network_slowdown
experiment_name_graph2_matmult = gen_global.experiment_name_poster_synmatmul
drawplots_fn(experiment_name_graph2_matmult, lambda x: x['job_completion_time'],
'job_completion_time', 'Job Completion Time [seconds]',
lambda x: x['object_transfer_time_cost'] == str(object_transfer_time_cost),
'Global Schedulers - Synthetic Matrix Multiplication 16,000x16,000 - slow {}'.format(network_slowdown),
'poster_figs/syn_matmult_global_slow_{}.png'.format(network_slowdown))
experiment_name_graph2_rnn = gen_global.experiment_name_poster_rnn
drawplots_fn(experiment_name_graph2_rnn, lambda x: x['job_completion_time'],
'job_completion_time', 'Job Completion Time [seconds]',
lambda x: x['object_transfer_time_cost'] == str(object_transfer_time_cost),
'Global Schedulers - RNN - slow {}'.format(network_slowdown),
'poster_figs/rnn_global_slow_{}.png'.format(network_slowdown))
experiment_name_graph2_rlpong = gen_global.experiment_name_poster_rlpong
drawplots_fn(experiment_name_graph2_rlpong, lambda x: x['job_completion_time'],
'job_completion_time', 'Job Completion Time [seconds]',
lambda x: x['object_transfer_time_cost'] == str(object_transfer_time_cost),
'Global Schedulers - RLPong - slow {}'.format(network_slowdown),
'poster_figs/rlpong_global_slow_{}.png'.format(network_slowdown))
def global_figs():
output_path = 'poster_figs'
if not os.path.exists(output_path):
os.makedirs(output_path)
to_graph = []
to_graph.append(('total', lambda x: x['max_cache_depth_size'] * x['num_nodes'],
'max_cache_depth_size', 'Total Cache Size Total [Bytes]'))
to_graph.append(('per_host', lambda x: x['max_cache_depth_size'],
'max_cache_depth_size', 'Total Cache Size Per Host [Bytes]'))
to_graph.append(('total_precise', lambda x: x['max_cache_precise_size'] * x['num_nodes'],
'max_cache_precise_size', 'Total Cache Size Total - Precise GC [Bytes]'))
to_graph.append(('per_host_precise', lambda x: x['max_cache_precise_size'],
'max_cache_precise_size', 'Total Cache Size Per Host - Precise GC [Bytes]'))
for (desc, y_variable_fn, y_variable_name, y_variable_description) in to_graph:
experiment_name_graph2_matmult = gen_global.experiment_name_poster_synmatmul
drawplots_fn(experiment_name_graph2_matmult, y_variable_fn, y_variable_name, y_variable_description,
lambda x: True,
'Global Schedulers - Synthetic Matrix Multiplication 16,000x16,000',
'poster_figs/syn_matmult_global_cache_{}.png'.format(desc))
experiment_name_graph2_rnn = gen_global.experiment_name_poster_rnn
drawplots_fn(experiment_name_graph2_rnn, y_variable_fn, y_variable_name, y_variable_description,
lambda x: True,
'Global Schedulers - RNN',
'poster_figs/rnn_global_cache_{}.png'.format(desc))
experiment_name_graph2_rlpong = gen_global.experiment_name_poster_rlpong
drawplots_fn(experiment_name_graph2_rlpong, y_variable_fn, y_variable_name, y_variable_description,
lambda x: True,
'Global Schedulers - RLPong',
'poster_figs/rlpong_global_cache_{}.png'.format(desc))
def poster_figs():
output_path = 'poster_figs'
if not os.path.exists(output_path):
os.makedirs(output_path)
experiment_name_graph2_matmult = poster_gen.experiment_name_poster_synmatmul
drawplots_fn(
experiment_name_graph2_matmult, lambda x: x['job_completion_time'],
'job_completion_time', 'Job Completion Time [seconds]',
lambda x: not x['scheduler'].endswith('local'),
'Global Schedulers - Synthetic Matrix Multiplication 16,000x16,000',
'poster_figs/syn_matmult_global.png')
experiment_name_graph2_rnn = poster_gen.experiment_name_poster_rnn
drawplots_fn(experiment_name_graph2_rnn,
lambda x: x['job_completion_time'], 'job_completion_time',
'Job Completion Time [seconds]',
lambda x: not x['scheduler'].endswith('local'),
'Global Schedulers - RNN', 'poster_figs/rnn_global.png')
experiment_name_graph2_rlpong = poster_gen.experiment_name_poster_rlpong
drawplots_fn(experiment_name_graph2_rlpong,
lambda x: x['job_completion_time'], 'job_completion_time',
'Job Completion Time [seconds]',
lambda x: not x['scheduler'].endswith('local'),
'Global Schedulers - RLPong', 'poster_figs/rlpong_global.png')
experiment_name_threshold = poster_gen.experiment_name_t1l
threshold_schedulers = set([
'trivial_threshold_local', 'transfer_aware_threshold_local',
'location_aware_threshold_local'
])
def plot_threshold(experiment_name, name, description, tracefile):
print "threshold plot {} {}".format(description, tracefile)
drawplots_generic(
experiment_name, lambda x: x['env']['RAY_SCHED_THRESHOLD1L'],
't1l', 'Threshold 1L', lambda x: x['job_completion_time'],
'job_completion_time', 'Job Completion Time [seconds]',
lambda x: x['scheduler'] in threshold_schedulers and x['tracefile']
== tracefile, 'Threshold - {}'.format(description),
'poster_figs/{}_threshold.png'.format(name))
plot_threshold(experiment_name_threshold, 'rnn', 'RNN',
poster_gen.trace_single_rnn)
plot_threshold(experiment_name_threshold, 'rlpong', 'RLPong',
poster_gen.trace_single_rlpong)
plot_threshold(experiment_name_threshold, 'synmatmul',
'Synthetic Matrix Multiplication',
poster_gen.trace_single_synmatmul)
# Figure 3
def plot_relative(experiment_name, ref_scheduler, plot_schedulers, name,
output_path):
drawplots_relative(experiment_name, lambda x: x['num_nodes'],
'num_nodes', 'Number of Nodes', ref_scheduler,
plot_schedulers, 'Local / Global - {}'.format(name),
output_path)
scheduler_combinations = [
('trivial', ['trivial_local', 'trivial_threshold_local']),
('transfer_aware',
['transfer_aware_local', 'transfer_aware_threshold_local'])
]
workloads = [(experiment_name_graph2_matmult, 'syn_matmult',
'Synthetic Matrix Multiplication 16,000x16,000'),
(experiment_name_graph2_rnn, 'rnn', 'RNN'),
(experiment_name_graph2_rlpong, 'rlpong', 'RLPong')]
for ref_scheduler, plot_schedulers in scheduler_combinations:
for experiment_name, workload, workload_desc in workloads:
plot_relative(
experiment_name, ref_scheduler, plot_schedulers, workload_desc,
'poster_figs/{}_{}_localvglobal.png'.format(
workload, ref_scheduler))