add table output to qdp result plotter which displays speedup compared to dram as baseline, redo the timing plots with the latest test results, add the speedup table

qdp_project/plots/table-qdpspeedup.tex

@@ -0,0 +1,10 @@
+\begin{tabular}{lll}
+\toprule
+Configuration & Speedup & Cache Hitrate \\
+\midrule
+DDR-SDRAM (Baseline) & x1.00 &  \textemdash  \\
+HBM (Upper Limit) & x1.41 &  \textemdash  \\
+Prefetching & x0.82 & 89.38 \% \\
+Prefetching, Distributed Columns & x1.23 & 93.20 \% \\
+\bottomrule
+\end{tabular}

qdp_project/plotter.py

@@ -1,14 +1,15 @@
 import os
 import csv
 import numpy as np
+import pandas as pd
 import seaborn as sns
 import matplotlib.pyplot as plt
 
 output_path = "./plots"
-hbm_result = "./evaluation-results/baseline/current-hbm/qdp-xeonmax-hbm-tca2-tcb0-tcj1-tmul16-wl4294967296-cs2097152.csv"
-dram_result = "./evaluation-results/baseline/current-dram/qdp-xeonmax-dram-tca2-tcb0-tcj1-tmul16-wl4294967296-cs2097152.csv"
-prefetch_result = "./evaluation-results/outofcacheallocation/qdp-xeonmax-prefetch-tca2-tcb1-tcj1-tmul16-wl4294967296-cs8388608.csv"
-distprefetch_result = "./evaluation-results/distprefetch/qdp-xeonmax-distprefetch-tca1-tcb1-tcj1-tmul32-wl4294967296-cs8388608.csv"
+hbm_result = "./evaluation-results/current/qdp-xeonmax-hbm-tca4-tcb0-tcj1-tmul32-wl4294967296-cs2097152.csv"
+dram_result = "./evaluation-results/current/qdp-xeonmax-dram-tca2-tcb0-tcj1-tmul32-wl4294967296-cs2097152.csv"
+prefetch_result = "./evaluation-results/current/qdp-xeonmax-prefetch-tca1-tcb1-tcj1-tmul32-wl4294967296-cs8388608.csv"
+distprefetch_result = "./evaluation-results/current/qdp-xeonmax-distprefetch-tca1-tcb1-tcj1-tmul32-wl4294967296-cs8388608.csv"
 
 tt_name = "rt-ns"
 function_names = [ "scana-run", "scanb-run", "aggrj-run" ]
@@ -31,24 +32,83 @@ def read_timings_from_csv(fname) -> tuple[list[float], list[str]]:
     return list(t.values()), list(t.keys())
 
 
-def get_data_prefetch_cache_access() -> tuple[list[float], list[str]]:
-    total = 0.47
-    data = [ 0.01, 0.01, 0.04, 0.42 ]
-    data = [ x * 100 / total for x in data ]
-    keys = ["Cache::GetCacheNode", "Cache::Access Itself", "dml::hardware_device::submit", "dml::make_mem_move_task (operator new)"]
+def read_total_time_from_csv(fname) -> float:
+    time = 0
+    row_count = 0
+    
+    with open(fname, newline='') as csvfile:
+        reader = csv.DictReader(csvfile, delimiter=';')
+        for row in reader:
+            row_count = row_count + 1
+            time += int(row["rt-ns"])
+
+    return time / (1000 * 1000 * row_count)
+
+
+def read_cache_hitrate_from_csv(fname) -> float:
+    hitrate = 0
+    row_count = 0
+    
+    with open(fname, newline='') as csvfile:
+        reader = csv.DictReader(csvfile, delimiter=';')
+        for row in reader:
+            row_count = row_count + 1
+            hitrate += float(row["cache-hr"])
+
+    return (hitrate * 100) / row_count
+
+
+def generate_speedup_table():
+    baseline = read_total_time_from_csv(dram_result)
+    columns = [ "Configuration", "Speedup", "Cache Hitrate" ]
+
+    names = [
+        "DDR-SDRAM (Baseline)",
+        "HBM (Upper Limit)",
+        "Prefetching",
+        "Prefetching, Distributed Columns"
+    ]
+
+    rawtime = [
+        read_total_time_from_csv(dram_result),
+        read_total_time_from_csv(hbm_result),
+        read_total_time_from_csv(prefetch_result),
+        read_total_time_from_csv(distprefetch_result),
+    ]
 
-    return data,keys
+    speedup = [
+        baseline / rawtime[0],
+        baseline / rawtime[1],
+        baseline / rawtime[2],
+        baseline / rawtime[3]
+    ]
 
-def get_data_prefetch_total() -> tuple[list[float], list[str]]:
-    return read_timings_from_csv(prefetch_result)
+    cachehr = [
+        0,
+        0,
+        read_cache_hitrate_from_csv(prefetch_result),
+        read_cache_hitrate_from_csv(distprefetch_result)
+    ]
+
+    data = [
+        [ names[0], f"x{speedup[0]:1.2f}", r" \textemdash " ],
+        [ names[1], f"x{speedup[1]:1.2f}", r" \textemdash " ],
+        [ names[2], f"x{speedup[2]:1.2f}", f"{cachehr[2]:2.2f} \%" ],
+        [ names[3], f"x{speedup[3]:1.2f}", f"{cachehr[3]:2.2f} \%" ]
+    ]
+
+    return pd.DataFrame(data, columns=columns)
+
+
+def tex_table(df, fname):
+    with open(os.path.join(output_path, fname), "w") as of:
+        of.write(df.to_latex(index=False))
 
-def get_data_dram_total() -> tuple[list[float], list[str]]:
-    return 
 
 # loops over all possible configuration combinations and calls
 # process_file_to_dataset for them in order to build a dataframe
 # which is then displayed and saved
-def main(data: tuple[list[float], list[str]], fname, unit):
+def donut_plot(data: tuple[list[float], list[str]], fname):
     palette_color = sns.color_palette('mako_r') 
     fig, ax = plt.subplots(figsize=(6, 3), subplot_kw=dict(aspect="equal"))
 
@@ -64,15 +124,19 @@ def main(data: tuple[list[float], list[str]], fname, unit):
         horizontalalignment = {-1: "right", 1: "left"}[int(np.sign(x))]
         connectionstyle = f"angle,angleA=0,angleB={ang}"
         kw["arrowprops"].update({"connectionstyle": connectionstyle})
-        ax.annotate(f"{data[1][i]} - {data[0][i]:2.2f} {unit}", xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y), horizontalalignment=horizontalalignment, **kw)    
+        ax.annotate(f"{data[1][i]} - {data[0][i]:2.2f} ms", xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y), horizontalalignment=horizontalalignment, **kw)    
     
     plt.rcParams.update({'font.size': 18})
     fig.savefig(os.path.join(output_path, fname), bbox_inches='tight')
 
 
+def main():
+    donut_plot(read_timings_from_csv(prefetch_result), "plot-timing-prefetch.pdf")
+    donut_plot(read_timings_from_csv(distprefetch_result), "plot-timing-distprefetch.pdf")
+    donut_plot(read_timings_from_csv(dram_result), "plot-timing-dram.pdf")
+    donut_plot(read_timings_from_csv(hbm_result), "plot-timing-hbm.pdf")
+    tex_table(generate_speedup_table(), "table-qdpspeedup.tex")
+
+
 if __name__ == "__main__":
-    main(get_data_prefetch_cache_access(), "plot-timing-cacheaccess.pdf", "%")
-    main(read_timings_from_csv(prefetch_result), "plot-timing-prefetch.pdf", "ms")
-    main(read_timings_from_csv(distprefetch_result), "plot-timing-distprefetch.pdf", "ms")
-    main(read_timings_from_csv(dram_result), "plot-timing-dram.pdf", "ms")
-    main(read_timings_from_csv(hbm_result), "plot-timing-hbm.pdf", "ms")
+    main()