diff --git a/launch_alg_bench.py b/launch_alg_bench.py
index dbf27ab..05a48f0 100755
--- a/launch_alg_bench.py
+++ b/launch_alg_bench.py
@@ -3,6 +3,8 @@ import subprocess
 from datetime import datetime
 
 ################ HELPER FUNCTIONS ################
+
+
 def load_template(template_path: str):
     output_template = ""
     with open(template_path, "r") as handle:
@@ -14,6 +16,7 @@ def write_batch(batch_fpath: str, batch_content: str):
     with open(batch_fpath, "w") as handle:
         _ = handle.write(batch_content)
 
+
 ################### SETUP DIRS ###################
 output_dir = os.getcwd()+"/output/"
 err_dir = os.getcwd()+"/error/"
diff --git a/results-and-plotting/.DS_Store b/results-and-plotting/.DS_Store
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index 0000000..01904e7
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diff --git a/results-and-plotting/plots/allgather_algcomp.png b/results-and-plotting/plots/allgather_algcomp.png
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index 0000000..2699d85
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diff --git a/results-and-plotting/plots/allgather_algcomp_log.png b/results-and-plotting/plots/allgather_algcomp_log.png
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index 0000000..a332063
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diff --git a/results-and-plotting/plots/allreduce_algcomp.png b/results-and-plotting/plots/allreduce_algcomp.png
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diff --git a/results-and-plotting/plots/allreduce_algcomp_log.png b/results-and-plotting/plots/allreduce_algcomp_log.png
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index 0000000..f48a5bb
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diff --git a/results-and-plotting/plots/alltoall_algcomp.png b/results-and-plotting/plots/alltoall_algcomp.png
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index 0000000..8a820fa
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diff --git a/results-and-plotting/plots/alltoall_algcomp_log.png b/results-and-plotting/plots/alltoall_algcomp_log.png
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index 0000000..d3da787
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diff --git a/results-and-plotting/plots/bcast_algcomp.png b/results-and-plotting/plots/bcast_algcomp.png
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index 0000000..13747db
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diff --git a/results-and-plotting/plots/bcast_algcomp_log.png b/results-and-plotting/plots/bcast_algcomp_log.png
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index 0000000..efac70d
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diff --git a/results-and-plotting/plots/gather_algcomp.png b/results-and-plotting/plots/gather_algcomp.png
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diff --git a/results-and-plotting/plots/gather_algcomp_log.png b/results-and-plotting/plots/gather_algcomp_log.png
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index 0000000..1924cb0
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diff --git a/results-and-plotting/plots/reduce_algcomp.png b/results-and-plotting/plots/reduce_algcomp.png
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diff --git a/results-and-plotting/plots/reduce_algcomp_log.png b/results-and-plotting/plots/reduce_algcomp_log.png
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index 0000000..9ff8f33
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diff --git a/results-and-plotting/plots/reduce_scatter_algcomp.png b/results-and-plotting/plots/reduce_scatter_algcomp.png
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index 0000000..26f32fa
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diff --git a/results-and-plotting/plots/reduce_scatter_algcomp_log.png b/results-and-plotting/plots/reduce_scatter_algcomp_log.png
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index 0000000..72fa2ae
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diff --git a/results-and-plotting/plots/scatter_algcomp.png b/results-and-plotting/plots/scatter_algcomp.png
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diff --git a/results-and-plotting/plots/scatter_algcomp_log.png b/results-and-plotting/plots/scatter_algcomp_log.png
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diff --git a/results-and-plotting/python/scripts/plot_alg.py b/results-and-plotting/python/scripts/plot_alg.py
new file mode 100644
index 0000000..c15b897
--- /dev/null
+++ b/results-and-plotting/python/scripts/plot_alg.py
@@ -0,0 +1,104 @@
+from matplotlib.ticker import FuncFormatter
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.lines import Line2D
+from enum import Enum
+
+
+class columns ():
+    benchmark_type = 'benchmark_type'
+    proc_num = 'proc_num'
+    msg_size_bytes = 'msg_size_bytes'
+    repetitions = 'repetitions'
+    t_min_usec = 't_min_usec'
+    t_max_usec = 't_max_usec'
+    t_avg_usec = 't_avg_usec'
+    mpi_datatype = 'mpi_datatype'
+    mpi_red_datatype = 'mpi_red_datatype'
+    mpi_red_op = 'mpi_red_op'
+    creation_time = 'creation_time'
+    n_nodes = 'n_nodes'
+    off_cache_flag = 'off_cache_flag'
+    algorithm = 'algorithm'
+
+
+class collectives(Enum):
+    Bcast = 'Bcast'
+    Reduce = 'Reduce'
+    Allreduce = 'Allreduce'
+    Alltoall = 'Alltoall'
+    Scatter = 'Scatter'
+    Reduce_scatter = 'Reduce_scatter'
+    Allgather = 'Allgather'
+    Gather = 'Gather'
+
+
+data_file = "./data/data_04_11_25_algs.csv"
+
+df_multinode = pd.read_csv(data_file, delimiter=',')
+df_multinode.fillna(0, inplace=True)
+df_multinode = df_multinode[df_multinode[columns.off_cache_flag] == 50]
+for c in collectives:
+
+    df_single = df_multinode[df_multinode[columns.benchmark_type]
+                             == c.value]
+    df_single = df_single[df_single[columns.msg_size_bytes] > 1000]
+    df_single = df_single[[columns.proc_num, columns.msg_size_bytes,
+                           columns.t_avg_usec, columns.algorithm]]
+
+    df_gather_best = df_single.loc[  # pyright: ignore
+        df_single.groupby([columns.msg_size_bytes, columns.proc_num])[  # pyright: ignore
+            columns.t_avg_usec].idxmin()
+    ].reset_index(drop=True)
+
+    df_gather_best = df_gather_best[df_gather_best[columns.msg_size_bytes] > 2**17]
+
+    pivot_best = df_gather_best.pivot(index=columns.msg_size_bytes,  # pyright: ignore
+                                      columns=columns.proc_num, values=columns.t_avg_usec)
+
+    X = pivot_best.columns.values   # proc_num
+    Y = pivot_best.index.values     # msg_size_bytes
+    X, Y = np.meshgrid(X, Y)   # pyright: ignore
+    Z = pivot_best.values
+
+    alg_pivot = df_gather_best.pivot(
+        index=columns.msg_size_bytes,
+        columns=columns.proc_num,
+        values=columns.algorithm
+    )
+
+    algorithms = alg_pivot.values.flatten()
+    unique_algs = sorted(pd.unique(algorithms[~pd.isna(algorithms)]))
+    color_map = {alg: i for i, alg in enumerate(unique_algs)}
+    color_values = np.array([color_map.get(a, np.nan) for a in algorithms])
+
+    fig = plt.figure(figsize=(16, 9))
+    ax = fig.add_subplot(111, projection='3d')
+    surf = ax.plot_wireframe(X, Y, Z,
+                             color='black', linewidths=1)
+    surf_points = ax.scatter(X,
+                             Y, Z, c=color_values, cmap='viridis', s=20, depthshade=False)  # pyright: ignore
+
+    handles = [
+        Line2D([0], [0],
+               marker='o', color='w',
+               label=alg,
+               markerfacecolor=plt.cm.viridis(
+                   color_map[alg] / max(len(unique_algs)-1, 1)),
+               markersize=8)
+        for alg in unique_algs
+    ]
+
+    ax.legend(handles=handles, title="Algorithm", loc='upper right')
+    ax.set_xlabel("Process Count")
+    ax.set_ylabel("Message Size [B]")
+    ax.set_zlabel("Average Time [μs]")
+    ax.set_title(f"{c.value}")
+    ax.set_xticks(pivot_best.columns.values)  # pyright: ignore
+    ax.set_xticklabels(pivot_best.columns.values)
+    ax.set_yticks(Y[:, 0])
+    ymin, ymax = ax.get_ylim()
+    ax.set_ylim(ymin*0.8, ymax)   # 30% more space at top
+    ax.set_yticklabels([f"$2^{{{int(np.log2(v))}}}$" for v in Y[:, 0]])
+    plt.savefig(f"./plots/{c.value.lower()}_algcomp.png")
diff --git a/results-and-plotting/python/scripts/plot_alg_log.py b/results-and-plotting/python/scripts/plot_alg_log.py
new file mode 100644
index 0000000..ac09e6e
--- /dev/null
+++ b/results-and-plotting/python/scripts/plot_alg_log.py
@@ -0,0 +1,127 @@
+from matplotlib.ticker import FuncFormatter
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.lines import Line2D
+from enum import Enum
+
+
+class columns ():
+    benchmark_type = 'benchmark_type'
+    proc_num = 'proc_num'
+    msg_size_bytes = 'msg_size_bytes'
+    repetitions = 'repetitions'
+    t_min_usec = 't_min_usec'
+    t_max_usec = 't_max_usec'
+    t_avg_usec = 't_avg_usec'
+    mpi_datatype = 'mpi_datatype'
+    mpi_red_datatype = 'mpi_red_datatype'
+    mpi_red_op = 'mpi_red_op'
+    creation_time = 'creation_time'
+    n_nodes = 'n_nodes'
+    off_cache_flag = 'off_cache_flag'
+    algorithm = 'algorithm'
+
+
+class collectives(Enum):
+    Bcast = 'Bcast'
+    Reduce = 'Reduce'
+    Allreduce = 'Allreduce'
+    Alltoall = 'Alltoall'
+    Scatter = 'Scatter'
+    Reduce_scatter = 'Reduce_scatter'
+    Allgather = 'Allgather'
+    Gather = 'Gather'
+
+
+def log_notation(val, pos):
+    return f" $1e{int(val)}$" if val != 0 else "1"
+
+
+def log2_notation(val, pos):
+    return "$2^{"+str(int(val))+"}$" if val != 0 else "1"
+
+
+data_file = "./data/data_04_11_25_algs.csv"
+
+df_multinode = pd.read_csv(data_file, delimiter=',')
+df_multinode.fillna(0, inplace=True)
+df_multinode = df_multinode[df_multinode[columns.off_cache_flag] == 50]
+for c in collectives:
+
+    df_single = df_multinode[df_multinode[columns.benchmark_type]
+                             == c.value]
+    df_single = df_single[df_single[columns.msg_size_bytes] > 1000]
+    df_single = df_single[[columns.proc_num, columns.msg_size_bytes,
+                           columns.t_avg_usec, columns.algorithm]]
+
+    df_gather_best = df_single.loc[  # pyright: ignore
+        df_single.groupby([columns.msg_size_bytes, columns.proc_num])[  # pyright: ignore
+            columns.t_avg_usec].idxmin()
+    ].reset_index(drop=True)
+
+    df_gather_worst = df_single.loc[  # pyright: ignore
+        df_single.groupby([columns.msg_size_bytes, columns.proc_num])[  # pyright: ignore
+            columns.t_avg_usec].idxmax()
+    ].reset_index(drop=True)
+    # df_gather_select = df_gather_select[df_gather_select[columns.msg_size_bytes] > 2**17]
+
+    pivot_best = df_gather_best.pivot(index=columns.msg_size_bytes,  # pyright: ignore
+                                      columns=columns.proc_num, values=columns.t_avg_usec)
+    pivot_worst = df_gather_worst.pivot(index=columns.msg_size_bytes,  # pyright: ignore
+                                        columns=columns.proc_num, values=columns.t_avg_usec)
+
+    X = pivot_best.columns.values   # proc_num
+    Y = pivot_best.index.values     # msg_size_bytes
+    X, Y = np.meshgrid(X, Y)   # pyright: ignore
+    Z = pivot_best.values
+
+    X_w = pivot_worst.columns.values   # proc_num
+    Y_w = pivot_worst.index.values     # msg_size_bytes
+    X_w, Y_w = np.meshgrid(X_w, Y_w)   # pyright: ignore
+    Z_w = pivot_worst.values
+
+    alg_pivot = df_gather_best.pivot(
+        index=columns.msg_size_bytes,
+        columns=columns.proc_num,
+        values=columns.algorithm
+    )
+
+    algorithms = alg_pivot.values.flatten()
+    unique_algs = sorted(pd.unique(algorithms[~pd.isna(algorithms)]))
+    color_map = {alg: i for i, alg in enumerate(unique_algs)}
+    color_values = np.array([color_map.get(a, np.nan) for a in algorithms])
+
+    fig = plt.figure(figsize=(16, 9))
+    ax = fig.add_subplot(111, projection='3d')
+    surf = ax.plot_wireframe(X, np.log2(Y), np.log(Z),
+                             color='black', linewidths=1)
+    surf = ax.plot_wireframe(X_w, np.log2(
+        Y_w), np.log(Z_w), color='gray', linewidths=0.3)
+
+    surf_points = ax.scatter(X, np.log2(
+        Y), np.log(Z), c=color_values, cmap='viridis', s=20, depthshade=False)  # pyright: ignore
+
+    surf_points = ax.scatter(X_w, np.log2(
+        Y_w), np.log(Z_w), c='gray', alpha=0.2,  s=20, depthshade=False)  # pyright: ignore
+
+    handles = [
+        Line2D([0], [0],
+               marker='o', color='w',
+               label=alg,
+               markerfacecolor=plt.cm.viridis(
+                   color_map[alg] / max(len(unique_algs)-1, 1)),
+               markersize=8)
+        for alg in unique_algs
+    ]
+
+    ax.legend(handles=handles, title="Algorithm", loc='upper right')
+    ax.set_xlabel("Process Count")
+    ax.set_ylabel("Message Size [B] (log2)")
+    ax.set_zlabel("Average Time [μs] (log10)")
+    ax.set_title(f"{c.value}")
+    ax.set_xticks(pivot_best.columns.values)  # pyright: ignore
+    ax.set_xticklabels(pivot_best.columns.values)
+    ax.yaxis.set_major_formatter(FuncFormatter(log2_notation))
+    ax.zaxis.set_major_formatter(FuncFormatter(log_notation))
+    plt.savefig(f"./plots/{c.value.lower()}_algcomp_log.png")