def plot_ipc(dict):
# Query 1
title = 'Instruction Per Clock Cycle Query 1'
ylabel = 'Instructions Per Clock Cycle'
xlabel = 'Combined Selectivity'
fig = init_fig(xlabel, ylabel, title)
fname = 'combined_graph_q1_ipc.png'
# Get the X-Points
x = get_x_points()
# Get the Y-Points
y_land_q1 = get_ipc(dict['compiled_result_land_q1'])
y_nb_q1 = get_ipc(dict['compiled_result_nb_q1'])
y_pand_q1 = get_ipc(dict['compiled_result_pand_q1'])
y_opt_q1 = get_ipc(dict['compiled_result_optimal_q1'])
#Plot the Combined Results
plot_combined_results(x, y_land_q1, y_nb_q1, y_pand_q1, y_opt_q1)
fig.savefig(fname)
# Query 2
title = 'Instructions Per Clock Cycle Query 2'
fig = init_fig(xlabel, ylabel, title)
fname = 'combined_graph_q2_ipc.png'
# Get the Y-Points
y_land_q2 = get_ipc(dict['compiled_result_land_q2'])
y_nb_q2 = get_ipc(dict['compiled_result_nb_q2'])
y_pand_q2 = get_ipc(dict['compiled_result_pand_q2'])
y_opt_q2 = get_ipc(dict['compiled_result_optimal_q2'])
#Plot the Combined Results
plot_combined_results(x, y_land_q2, y_nb_q2, y_pand_q2, y_opt_q2)
fig.savefig(fname)
# Query 3
title = 'Instructions Per Clock Cycle Query 3'
fig = init_fig(xlabel, ylabel, title)
fname = 'combined_graph_q3_ipc.png'
x = get_query3_x_points()
# Get the Y-Points
y_land_q3 = get_ipc(dict['compiled_result_land_q3'])
y_nb_q3 = get_ipc(dict['compiled_result_nb_q3'])
y_pand_q3 = get_ipc(dict['compiled_result_pand_q3'])
y_opt_q3 = get_ipc(dict['compiled_result_optimal_q3'])
#Plot the Combined Results
plot_combined_results(x, y_land_q3, y_nb_q3, y_pand_q3, y_opt_q3)
fig.savefig(fname)
def plot_elapsed_time(dict):
# Query 1
title = 'Elapsed Time Query 1'
ylabel = 'Elapsed Time (seconds)'
xlabel = 'Combined Selectivity'
fig = init_fig(xlabel, ylabel, title)
fname = 'combined_graph_q1_elapsed_time.png'
# Get the X-Points
x = get_x_points()
# Get the Y-Points
y_land_q1 = get_elapsed_time(dict['compiled_result_land_q1'])
y_nb_q1 = get_elapsed_time(dict['compiled_result_nb_q1'])
y_pand_q1 = get_elapsed_time(dict['compiled_result_pand_q1'])
y_opt_q1 = get_elapsed_time(dict['compiled_result_optimal_q1'])
#Plot the Combined Results
plot_combined_results(x, y_land_q1, y_nb_q1, y_pand_q1, y_opt_q1)
fig.savefig(fname)
# Query 2
title = 'Elapsed Time Query 2'
fig = init_fig(xlabel, ylabel, title)
fname = 'combined_graph_q2_elapsed_time.png'
# Get the Y-Points
y_land_q2 = get_elapsed_time(dict['compiled_result_land_q2'])
y_nb_q2 = get_elapsed_time(dict['compiled_result_nb_q2'])
y_pand_q2 = get_elapsed_time(dict['compiled_result_pand_q2'])
y_opt_q2 = get_elapsed_time(dict['compiled_result_optimal_q2'])
#Plot the Combined Results
plot_combined_results(x, y_land_q2, y_nb_q2, y_pand_q2, y_opt_q2)
fig.savefig(fname)
# Query 3
title = 'Elapsed Time Query 3'
fig = init_fig(xlabel, ylabel, title)
fname = 'combined_graph_q3_elapsed_time.png'
x = get_query3_x_points()
# Get the Y-Points
y_land_q3 = get_elapsed_time(dict['compiled_result_land_q3'])
y_nb_q3 = get_elapsed_time(dict['compiled_result_nb_q3'])
y_pand_q3 = get_elapsed_time(dict['compiled_result_pand_q3'])
y_opt_q3 = get_elapsed_time(dict['compiled_result_optimal_q3'])
#Plot the Combined Results
plot_combined_results(x, y_land_q3, y_nb_q3, y_pand_q3, y_opt_q3)
fig.savefig(fname)
def plot_mispredict_rate(dict):
# Query 1
title = 'Branch MisPrediction Rate Query 1'
ylabel = '% Branch Misprediction Rate'
xlabel = 'Combined Selectivity'
fig = init_fig(xlabel, ylabel, title)
fname = 'combined_graph_q1_mispredict_rate.png'
# Get the X-Points
x = get_x_points()
# Get the Y-Points
y_land_q1 = get_mispredict_rate(dict['compiled_result_land_q1'])
y_nb_q1 = get_mispredict_rate(dict['compiled_result_nb_q1'])
y_pand_q1 = get_mispredict_rate(dict['compiled_result_pand_q1'])
y_opt_q1 = get_mispredict_rate(dict['compiled_result_optimal_q1'])
#Plot the Combined Results
plot_combined_results(x, y_land_q1, y_nb_q1, y_pand_q1, y_opt_q1)
fig.savefig(fname)
# Query 2
title = 'Branch MisPrediction Rate Query 2'
fig = init_fig(xlabel, ylabel, title)
fname = 'combined_graph_q2_mispredict_rate.png'
# Get the Y-Points
y_land_q2 = get_mispredict_rate(dict['compiled_result_land_q2'])
y_nb_q2 = get_mispredict_rate(dict['compiled_result_nb_q2'])
y_pand_q2 = get_mispredict_rate(dict['compiled_result_pand_q2'])
y_opt_q2 = get_mispredict_rate(dict['compiled_result_optimal_q2'])
#Plot the Combined Results
plot_combined_results(x, y_land_q2, y_nb_q2, y_pand_q2, y_opt_q2)
fig.savefig(fname)
# Query 3
title = 'Branch MisPrediction Rate Query 3'
fig = init_fig(xlabel, ylabel, title)
fname = 'combined_graph_q3_mispredict_rate.png'
x = get_query3_x_points()
# Get the Y-Points
y_land_q3 = get_mispredict_rate(dict['compiled_result_land_q3'])
y_nb_q3 = get_mispredict_rate(dict['compiled_result_nb_q3'])
y_pand_q3 = get_mispredict_rate(dict['compiled_result_pand_q3'])
y_opt_q3 = get_mispredict_rate(dict['compiled_result_optimal_q3'])
#Plot the Combined Results
plot_combined_results(x, y_land_q3, y_nb_q3, y_pand_q3, y_opt_q3)
fig.savefig(fname)
def plot_predicted(result_dict, predicted_dict):
# Query 1
title = 'Predicted and Actual Performance Query 1'
ylabel = 'CPU Cycles per record'
xlabel = 'Combined Selectivity'
fig = init_fig(xlabel, ylabel, title)
base_fname = 'predicted_perf_graph_q%d.png'
fname = base_fname % 1
# Get the X-Points
x = get_x_points()
# Get the Y-Points
y_land_q1 = get_cpu_cycles(result_dict['compiled_result_land_q1'])
y_pand_q1 = get_cpu_cycles(result_dict['compiled_result_pand_q1'])
y_opt_q1 = get_cpu_cycles(result_dict['compiled_result_optimal_q1'])
y_pred_q1 = predicted_dict['predicted_runtime_cpu_cycles_q1']
#Plot the Combined Results
plot_combined_predicted_results(x, y_land_q1, y_pred_q1, y_pand_q1, y_opt_q1)
fig.savefig(fname)
# Query 2
title = 'Predicted and Actual Performance Query 2'
ylabel = 'CPU Cycles per record'
xlabel = 'Combined Selectivity'
fig = init_fig(xlabel, ylabel, title)
base_fname = 'predicted_perf_graph_q%d.png'
fname = base_fname % 2
# Get the X-Points
x = get_x_points()
# Get the Y-Points
y_land_q2 = get_cpu_cycles(result_dict['compiled_result_land_q2'])
y_pand_q2 = get_cpu_cycles(result_dict['compiled_result_pand_q2'])
y_opt_q2 = get_cpu_cycles(result_dict['compiled_result_optimal_q2'])
y_pred_q2 = predicted_dict['predicted_runtime_cpu_cycles_q2']
#Plot the Combined Results
plot_combined_predicted_results(x, y_land_q2, y_pred_q2, y_pand_q2, y_opt_q2)
fig.savefig(fname)
# Query 3
title = 'Predicted and Actual Performance Query 3'
ylabel = 'CPU Cycles per record'
xlabel = 'Combined Selectivity'
fig = init_fig(xlabel, ylabel, title)
base_fname = 'predicted_perf_graph_q%d.png'
fname = base_fname % 3
# Get the X-Points
x = get_query3_x_points()
# Get the Y-Points
y_land_q3 = get_cpu_cycles(result_dict['compiled_result_land_q3'])
y_pand_q3 = get_cpu_cycles(result_dict['compiled_result_pand_q3'])
y_opt_q3 = get_cpu_cycles(result_dict['compiled_result_optimal_q3'])
y_pred_q3 = predicted_dict['predicted_runtime_cpu_cycles_q3']
#Plot the Combined Results
plot_combined_predicted_results(x, y_land_q3, y_pred_q3, y_pand_q3, y_opt_q3)
fig.savefig(fname)
