def execute_result_formats(runs=1, sel=0.1):
announce_experiment(f'Result Formats')
set_default_parameters(
{'result_format': 0, 'run_count': runs, 'clear_cache': 1, 'cache_size': 50, 'pcm_set': 1, 'random_values': 1,
'column_size': 2e7, 'selectivity': sel, 'reserve_memory': 0, 'use_if': 1, 'n_cores': 3, 'jobs_per_core': 1})
data = generate_data(
[{'xParam': 'result_format', 'xMin': 0, 'xMax': 2, 'stepSize': 1}])
store_results(data)
def execute_cache_misses():
announce_experiment(f'Cache Misses')
# TESTME: use_if=1 should increase the effect since no preloading should be possible
set_default_parameters(
{'result_format': 0, 'run_count': 500, 'clear_cache': 1, 'cache_size': 40, 'pcm_set': 0, 'random_values': 1,
'column_size': 2e8, 'selectivity': 0.1, 'reserve_memory': 1, 'use_if': 1, 'n_cores': 1, 'jobs_per_core': 1})
data = generate_data(
[{'xParam': 'column_size', 'xMin': 2, 'xMax': 8, 'stepSize': 1e5, 'log': True, 'logSamples': 20}])
store_results(data)
def execute_multicore(runs=100):
announce_experiment(f'Multicore')
# result_format=0 is the fastest one
# TESTME: Run with higher column size and/or run_count
set_default_parameters(
{'result_format': 0, 'run_count': runs, 'clear_cache': 0, 'cache_size': 10, 'pcm_set': 1, 'random_values': 1,
'column_size': 2e7, 'selectivity': 0.1, 'reserve_memory': 0, 'use_if': 1, 'n_cores': 2, 'jobs_per_core': 1})
# data = generate_data([{'xParam': 'n_cores', 'xMin': 1, 'xMax': 80, 'stepSize': 1}])
data = generate_data([{'xParam': 'n_cores', 'xMin': 1, 'xMax': 20, 'stepSize': 1}])
store_results(data)
def execute_selectivity():
announce_experiment(f'Selectivity (Bell Plot)')
# Not working for result_format=0
# TESTME: Higher stepSize for selectivity
# TESTME: Compare all result_formats
set_default_parameters(
{'result_format': 2, 'run_count': 5, 'clear_cache': 0, 'cache_size': 40, 'pcm_set': 1, 'random_values': 1,
'column_size': 1e8, 'selectivity': 0.1, 'reserve_memory': 0, 'use_if': 1, 'n_cores': 1, 'jobs_per_core': 1})
data = generate_data(
[{'xParam': 'random_values', 'xMin': 0, 'xMax': 1, 'stepSize': 1},
{'xParam': 'selectivity', 'xMin': 0, 'xMax': 1, 'stepSize': 0.05}])
store_results(data)
def execute_test_run():
set_default_parameters(
{'result_format': 1, 'run_count': 100, 'clear_cache': 0, 'cache_size': 10, 'pcm_set': 0, 'random_values': 1,
'column_size': 20000000, 'selectivity': 0.1, 'reserve_memory': 0, 'use_if': 0, 'n_cores': 1, 'jobs_per_core': 1})
data = generate_data(
[{'xParam': 'result_format', 'xMin': 0, 'xMax': 2, 'stepSize': 1},
{'xParam': 'n_cores', 'xMin': 1, 'xMax': 3, 'stepSize': 1, 'n_runs': 1}],
# {'xParam': 'column_size', 'xMin': 1, 'xMax': 1000, 'stepSize': 100 }],
)
path = store_results(data)
# path = None
data = load_results(path)
generate_plots(data, y1_label='gb_per_sec')
def execute_benchmarks(runs):
# cache misses over column size
set_default_parameters(
{'result_format': 0, 'run_count': runs, 'clear_cache': 0, 'cache_size': 10,
'pcm_set': 0, 'random_values': 1, 'column_size': 200000000, 'selectivity': 0.1,
'reserve_memory': 0, 'use_if': 0, 'n_cores': 1, 'jobs_per_core': 1})
data1 = generate_data(
[{'xParam': 'result_format', 'xMin': 0, 'xMax': 3, 'stepSize': 1},
{'xParam': 'column_size', 'xMin': 10000000, 'xMax': 200000000, 'stepSize': 10000000}],
'gb_per_sec', 'l1_cache_misses')
store_results(data1)
generate_plots(data1)
data2 = generate_data(
[{'xParam': 'result_format', 'xMin': 0, 'xMax': 3, 'stepSize': 1},
{'xParam': 'column_size', 'xMin': 10000000, 'xMax': 200000000, 'stepSize': 10000000}],
'gb_per_sec', 'l2_cache_misses')
store_results(data2)
generate_plots(data2)
data3 = generate_data(
[{'xParam': 'result_format', 'xMin': 0, 'xMax': 3, 'stepSize': 1},
{'xParam': 'column_size', 'xMin': 10000000, 'xMax': 200000000, 'stepSize': 10000000}],
'gb_per_sec', 'l3_cache_misses')
store_results(data3)
generate_plots(data3)
# cache misses comparison random vs not random
data4 = generate_data(
[{'xParam': 'result_format', 'xMin': 0, 'xMax': 3, 'stepSize': 1},
{'xParam': 'random_values', 'xMin': 0, 'xMax': 1, 'stepSize': 1}],
'gb_per_sec', 'l1_cache_misses')
store_results(data4)
generate_plots(data4)
data5 = generate_data(
[{'xParam': 'result_format', 'xMin': 0, 'xMax': 3, 'stepSize': 1},
{'xParam': 'random_values', 'xMin': 0, 'xMax': 1, 'stepSize': 1}],
'gb_per_sec', 'l2_cache_misses')
store_results(data5)
generate_plots(data5)
data6 = generate_data(
[{'xParam': 'result_format', 'xMin': 0, 'xMax': 3, 'stepSize': 1},
{'xParam': 'random_values', 'xMin': 0, 'xMax': 1, 'stepSize': 1}],
'gb_per_sec', 'l3_cache_misses')
store_results(data6)
generate_plots(data6)
# cache misses for reserve memory
set_default_parameters(
{'result_format': 1, 'run_count': 25, 'clear_cache': 0, 'cache_size': 10,
'pcm_set': 0, 'random_values': 1, 'column_size': 200000000, 'selectivity': 0.1,
'reserve_memory': 0, 'use_if': 0, 'n_cores': 1, 'jobs_per_core': 1})
data7 = generate_data(
[{'xParam': 'reserve_memory', 'xMin': 0, 'xMax': 1, 'stepSize': 1},
{'xParam': 'column_size', 'xMin': 10000000, 'xMax': 200000000, 'stepSize': 10000000}],
'gb_per_sec', 'l1_cache_misses')
store_results(data7)
generate_plots(data7)
data8 = generate_data(
[{'xParam': 'reserve_memory', 'xMin': 0, 'xMax': 1, 'stepSize': 1},
{'xParam': 'column_size', 'xMin': 10000000, 'xMax': 200000000, 'stepSize': 10000000}],
'gb_per_sec', 'l2_cache_misses')
store_results(data8)
generate_plots(data8)
data9 = generate_data(
[{'xParam': 'reserve_memory', 'xMin': 0, 'xMax': 1, 'stepSize': 1},
{'xParam': 'column_size', 'xMin': 10000000, 'xMax': 200000000, 'stepSize': 10000000}],
'gb_per_sec', 'l3_cache_misses')
store_results(data9)
generate_plots(data9)
# branch predictions for selectivity
set_default_parameters(
{'result_format': 1, 'run_count': 25, 'clear_cache': 0, 'cache_size': 10,
'pcm_set': 1, 'random_values': 1, 'column_size': 200000000, 'selectivity': 0.1,
'reserve_memory': 0, 'use_if': 0, 'n_cores': 1, 'jobs_per_core': 1})
data10 = generate_data(
[{'xParam': 'result_format', 'xMin': 0, 'xMax': 3, 'stepSize': 1},
{'xParam': 'selectivity', 'xMin': 0, 'xMax': 1, 'stepSize': .1}],
'gb_per_sec', 'branch_mispredictions')
store_results(data10)
generate_plots(data10)
# branch predictions and stalled cycles for selectivity
data11 = generate_data(
[{'xParam': 'result_format', 'xMin': 0, 'xMax': 3, 'stepSize': 1},
{'xParam': 'selectivity', 'xMin': 0, 'xMax': 1, 'stepSize': .1}],
'branch_mispredictions', 'stalled_cycles')
store_results(data11)
generate_plots(data11)
# branch predictions for selectivity with values at beginning
set_default_parameters(
{'result_format': 1, 'run_count': 25, 'clear_cache': 0, 'cache_size': 10,
'pcm_set': 1, 'random_values': 0, 'column_size': 200000000, 'selectivity': 0.1,
'reserve_memory': 0, 'use_if': 0, 'n_cores': 1, 'jobs_per_core': 1})
data12 = generate_data(
[{'xParam': 'result_format', 'xMin': 0, 'xMax': 3, 'stepSize': 1},
{'xParam': 'selectivity', 'xMin': 0, 'xMax': 1, 'stepSize': .1}],
'gb_per_sec', 'branch_mispredictions')
store_results(data12)
generate_plots(data12)
# branch mispredictions with if
set_default_parameters(
{'result_format': 3, 'run_count': 25, 'clear_cache': 0, 'cache_size': 10,
'pcm_set': 1, 'random_values': 1, 'column_size': 200000000, 'selectivity': 0.1,
'reserve_memory': 0, 'use_if': 0, 'n_cores': 1, 'jobs_per_core': 1})
data13 = generate_data(
[{'xParam': 'use_if', 'xMin': 0, 'xMax': 1, 'stepSize': 1},
{'xParam': 'selectivity', 'xMin': 0, 'xMax': 1, 'stepSize': .1}],
'gb_per_sec', 'branch_mispredictions')
store_results(data13)
generate_plots(data13)
# accumulate gb for increasing cores
set_default_parameters(
{'result_format': 1, 'run_count': 25, 'clear_cache': 0, 'cache_size': 10, 'pcm_set': 1, 'random_values': 1,
'column_size': 20000000, 'selectivity': 0.1, 'reserve_memory': 0, 'use_if': 0, 'n_cores': 2, 'jobs_per_core': 1})
data14 = generate_data(
[{'xParam': 'n_cores', 'xMin': 1, 'xMax': 70, 'stepSize': 4}],
'gb_per_sec', 'stalled_cycles') # 'selectivity'
store_results(data14)
generate_plots(data14)
data15 = generate_data(
[{'xParam': 'result_format', 'xMin': 0, 'xMax': 3, 'stepSize': 1},
{'xParam': 'n_cores', 'xMin': 1, 'xMax': 70, 'stepSize': 4}],
'gb_per_sec', 'stalled_cycles') # 'selectivity'
store_results(data15)
generate_plots(data15)