Wallclock time plots

configs/pim_config.py

@@ -7,7 +7,7 @@ class Configuration:
     workload: str
     executable: Path
     level: str
-    pim: bool
+    system: str
     frequency: str = "3GHz"
 
 @dataclass(frozen=True)

latex_table.py

@@ -0,0 +1,45 @@
+import matplotlib.pyplot as plt
+import seaborn as sns
+import polars as pl
+import numpy as np
+
+from pathlib import Path
+
+workload_order = {val: idx for idx, val in enumerate(["vadd", "vmul", "haxpy", "gemv", "gemv_layers"])}
+
+workload_mapping = {
+    "vadd": "VADD",
+    "vmul": "VMUL",
+    "haxpy": "HAXPY",
+    "gemv": "GEMV",
+    "gemv_layers": "DNN",
+}
+
+out_directory = Path("tables_out")
+
+df = pl.read_csv("pim_results.csv")
+df = df.select(["workload", "level", "system", "frequency", "ticks"])
+
+for name, data in df.group_by(["frequency"], maintain_order=True):
+    data = data.pivot(index=["workload", "level"], columns=["system"], values=["ticks"])
+    data = data.sort(pl.col("workload").replace(workload_order))
+    data = data.with_columns(pl.col("workload").replace(workload_mapping))
+    data = data.rename({"HBM": "hbm", "PIM-HBM": "pim"})
+    print(data)
+
+    data.write_csv(out_directory / f"simulations_{name[0]}.csv")
+
+vega_df = pl.read_csv("vega_results.csv")
+vega_df = vega_df.with_columns(system=pl.lit("vega"))
+
+tesla_df = pl.read_csv("tesla_results.csv")
+tesla_df = tesla_df.with_columns(system=pl.lit("tesla"))
+
+torch_df = pl.concat([vega_df, tesla_df])
+
+torch_df = torch_df.pivot(index=["workload", "level"], columns=["system"], values=["runtime"])
+torch_df = torch_df.sort(pl.col("workload").replace(workload_order))
+torch_df = torch_df.with_columns(pl.col("workload").replace(workload_mapping))
+print(torch_df)
+
+torch_df.write_csv(out_directory / "torch.csv")

pim_plots.py

@@ -1,48 +1,64 @@
-import matplotlib.pyplot as plt
+import polars as pl
 import seaborn as sns
-import pandas as pd
+import matplotlib.pyplot as plt
-import numpy as np
+from datetime import datetime
 
 from pathlib import Path
 
-df = pd.read_csv("pim_results.csv")
+out_directory = Path("pim_plots_out")
 
-sns.set_theme()
+df = pl.read_csv("pim_results.csv")
 
-def calc_speedup(x):
+workload_sets = {
-    return x.iat[0] / x.iat[1]
+    "vector": ["vadd", "vmul", "haxpy"],
+    "matrix": ["gemv", "dnn"],
+}
 
-workload_sets = [["vadd", "vmul", "haxpy"], ["gemv", "gemv_layers"]]
+workload_mapping = {
+    "gemv_layers": "dnn",
+}
 
-for workload_set in workload_sets:
+system_mapping = {
-    workload_filter = df["workload"].isin(workload_set)
+    "HBM": "hbm",
+    "PIM-HBM": "pim"
+}
 
-    for frequency in df["frequency"].unique():
+def calc_speedup(tick_list):
-        frequency_filter = df["frequency"] == frequency
+    return tick_list[0] / tick_list[1]
 
-        filtered_df = df[workload_filter & frequency_filter]
-        print(filtered_df)
-        preprocessed_df = filtered_df.groupby(["workload", "level", "frequency"], as_index=False).agg({"ticks": calc_speedup}).rename(columns={"ticks":"speedup"})
 
-        print(preprocessed_df)
+df = df.with_columns(pl.col("workload").replace(workload_mapping))
-        # preprocessed_df.to_csv("plot.csv", index=False)
+df = df.with_columns(pl.col("system").replace(system_mapping))
 
-        g = sns.catplot(
+df = df.group_by(
-            data=preprocessed_df, kind="bar",
+    ["workload", "level", "frequency"], maintain_order=True
-            x="level", y="speedup", hue="workload",
+).agg(pl.col("ticks").map_elements(calc_speedup).alias("speedup"))
-            palette="dark", alpha=.6, height=6
+
+for name, data in df.group_by(
+    "frequency",
+    pl.when(pl.col("workload").is_in(workload_sets["vector"]))
+    .then(pl.lit("vector"))
+    .when(pl.col("workload").is_in(workload_sets["matrix"]))
+    .then(pl.lit("matrix")),
+):
+    plot = sns.catplot(
+        data=data.to_pandas(),
+        kind="bar",
+        x="level",
+        y="speedup",
+        hue="workload",
+        palette="dark",
+        alpha=0.6,
+        height=6,
     )
+    plot.set_axis_labels("Level", "Speedup")
+    plot.set(title=name[0] + name[1])
 
-        g.despine(left=True)
+    plot.fig.subplots_adjust(top=0.95)
-        g.set_axis_labels("", "Speedup")
-        g.set(title=frequency)
-        g.legend.set_title("")
 
-        for workload in workload_set:
+    data = data.pivot(index=["level"], columns=["workload"], values=["speedup"])
-            export_df = preprocessed_df[preprocessed_df["workload"] == workload]
+    print(data)
 
-            filename = f"{workload}_{frequency}.csv"
+    data.write_csv(out_directory / f"{name[1]}_{name[0]}.csv")
-            directory = Path("plots_out")
-            export_df.to_csv(directory / filename, index=False)
 
 plt.show()

simulation_script.py

@@ -1,13 +1,13 @@
 import subprocess
-import csv
-import copy
 import dataclasses
 import json
 import pandas as pd
 
+from tqdm import tqdm
+from dataclasses import dataclass
 from threading import Thread
+from multiprocessing.pool import ThreadPool
 from pathlib import Path
-from typing import Dict
 from configs.pim_config import Configuration, Statistics
 
 gem5 = Path("build/ARM/gem5.opt")
@@ -15,17 +15,17 @@ out_dir_base = Path("pim_out")
 pim_simulation = Path("configs/pim_simulation.py")
 
 
-class Gem5Thread(Thread):
+@dataclass
-    def __init__(self, configuration: Configuration) -> None:
+class WorkItem:
-        super().__init__()
+    configuration: Configuration
-        self.configuration = configuration
+    statistics: Statistics | None = None
 
-    def run(self):
+def run_gem5_process(work_item: WorkItem):
     serialized_configuration = json.dumps(
-            dataclasses.asdict(self.configuration)
+        dataclasses.asdict(work_item.configuration)
     )
 
-        out_dir = out_dir_base / configuration.name
+    out_dir = out_dir_base / work_item.configuration.name
 
     out = subprocess.run(
         [
@@ -38,8 +38,7 @@ class Gem5Thread(Thread):
     )
 
     output = out.stdout.splitlines()[-1]
-        self.statistics = Statistics(**json.loads(output))
+    work_item.statistics = Statistics(**json.loads(output))
-
 
 workload_base_directory = Path("kernels")
 workload_sub_directory = Path("aarch64-unknown-none/release")
@@ -60,7 +59,9 @@ systems = [
 configurations: list[Configuration] = []
 
 for frequency in ["3GHz", "100GHz"]:
+# for frequency in ["100GHz"]:
     for level in ["X1", "X2", "X3", "X4"]:
+    # for level in ["X3"]:
         for system in systems:
             for workload in workloads:                
                 executable = workload
@@ -77,28 +78,26 @@ for frequency in ["3GHz", "100GHz"]:
 
                 configurations.append(
                     Configuration(
-                        f"{workload}_{level}_{frequency}",
+                        f"{workload}_{level}_{system}_{frequency}",
                         workload,
                         executable.as_posix(),
                         level,
-                        system == "PIM-HBM",
+                        system,
                         frequency,
                     )
                 )
 
-threads: list[Gem5Thread] = []
+work_items = [WorkItem(configuration) for configuration in configurations]
+
+with ThreadPool() as pool:
+    for _ in tqdm(pool.imap_unordered(run_gem5_process, work_items), total=len(work_items)):
+        pass
 
-for configuration in configurations:
-    thread = Gem5Thread(configuration)
-    thread.start()
-    threads.append(thread)
 
 results: list[dict] = []
 
-for thread in threads:
+for work_item in work_items:
-    thread.join()
+    result = dataclasses.asdict(work_item.configuration) | dataclasses.asdict(work_item.statistics)
-
-    result = dataclasses.asdict(thread.configuration) | dataclasses.asdict(thread.statistics)
     results.append(result)
 
 dataframe = pd.DataFrame(results)

torch_plots.py

@@ -0,0 +1,59 @@
+import matplotlib.pyplot as plt
+import seaborn as sns
+import polars as pl
+
+from pathlib import Path
+
+out_directory = Path("torch_plots_out")
+
+system_mapping = {
+    "HBM": "hbm",
+    "PIM-HBM": "pim"
+}
+
+gem_df = pl.read_csv("pim_results.csv")
+gem_df = gem_df.with_columns(pl.col("system").replace(system_mapping))
+gem_df = gem_df.with_columns(
+    pl.concat_str(["system", "frequency"], separator="_").alias("system")
+)
+gem_df = gem_df.select(["workload", "level", "system", "ticks"])
+
+vega_df = pl.read_csv("vega_results.csv")
+vega_df = vega_df.rename({"runtime": "ticks"})
+vega_df = vega_df.with_columns(pl.lit("vega").alias("system"))
+
+tesla_df = pl.read_csv("tesla_results.csv")
+tesla_df = tesla_df.rename({"runtime": "ticks"})
+tesla_df = tesla_df.with_columns(pl.lit("tesla").alias("system"))
+
+df = pl.concat([gem_df, vega_df, tesla_df], how="diagonal")
+
+workload_sets = [["vadd", "vmul", "haxpy"], ["gemv", "dnn"]]
+
+workload_mapping = {
+    "gemv_layers": "dnn",
+}
+
+df = df.with_columns(pl.col("workload").replace(workload_mapping))
+
+# for workload_set in workload_sets:
+#     temp_df = df.filter(pl.col("workload").is_in(workload_set))
+
+g = sns.catplot(
+    data=df.to_pandas(),
+    kind="bar",
+    x="level",
+    y="ticks",
+    hue="system",
+    col="workload",
+    palette="dark",
+    alpha=0.6,
+    height=6,
+)
+
+for name, data in df.group_by("system"):
+    data = data.pivot(index=["level"], columns=["workload"], values=["ticks"])
+    data.write_csv(out_directory / f"{name}.csv")
+    print(data)
+
+plt.show()

wallclock_time_plots.py

@@ -0,0 +1,52 @@
+import re
+import polars as pl
+import seaborn as sns
+import matplotlib.pyplot as plt
+from datetime import timedelta
+
+from pathlib import Path
+
+stats_dir = Path("pim_out")
+
+runtime_dict: dict[str, list[any]] = {}
+
+for element in stats_dir.iterdir():
+    print(element.name)
+    matches = re.search(r'(\w+)_(\w+)_(\w*-*\w*)_(\w+)', element.name)
+    workload, level, system, freq = matches.group(1), matches.group(2), matches.group(3), matches.group(4)
+
+    with open(element / "stats.txt") as f:
+        regex = re.compile(r'hostSeconds\ +(\d+.\d+).*')
+        for line in f:
+            result = regex.search(line)
+            if result is not None:
+                # implicitly only get last match in file...
+                runtime = result.group(1)
+
+    runtime_dict.setdefault("workload", []).append(workload)
+    runtime_dict.setdefault("level", []).append(level)
+    runtime_dict.setdefault("system", []).append(system)
+    runtime_dict.setdefault("freq", []).append(freq)
+    runtime_dict.setdefault("runtime", []).append(float(runtime))
+
+df = pl.DataFrame(runtime_dict)
+df = df.filter((pl.col("freq") == "100GHz") & (pl.col("level") == "X3"))
+df = df.drop("freq")
+print(df)
+
+plot = sns.catplot(
+    data=df.to_pandas(),
+    kind="bar",
+    x="system",
+    y="runtime",
+    hue="workload",
+    palette="dark",
+    alpha=0.6,
+    height=6,
+)
+plot.set_axis_labels("PIM vs. Non-PIM", "Runtime [s]")
+plot.set(title="Wallclock Time")
+
+plot.fig.subplots_adjust(top=0.95)
+
+plt.show()