diff --git a/pipit/trace.py b/pipit/trace.py
index 2b4f111a..5d5cbe08 100644
--- a/pipit/trace.py
+++ b/pipit/trace.py
@@ -861,3 +861,155 @@ def detect_pattern(
             patterns.append(match_original)
 
         return patterns
+
+    # TODO: comments
+    # TODO: unit tests - compare with hta tool
+    def comm_comp_breakdown(self):
+        def kernel_type(name):
+            if "ncclKernel" in name:
+                return "comm"
+            elif "Memcpy" in name or "Memset" in name:
+                return "mem"
+            else:
+                return "comp"
+
+        def merge_intervals(intervals):
+            merged_intervals = [intervals.pop(0)]
+
+            while len(intervals) > 0:
+                top_start, top_end = merged_intervals[-1]
+                curr_start, curr_end = intervals.pop(0)
+
+                if curr_end >= top_start and curr_start <= top_end:
+                    merged_intervals[-1] = (
+                        min([curr_start, top_start]),
+                        max([curr_end, top_end]),
+                    )
+                else:
+                    merged_intervals.append((curr_start, curr_end))
+
+            return merged_intervals
+
+        def mean_over_ranks(breakdown_dict):
+            mean_dict = {
+                "Total Time": 0,
+                "Only Comp": 0,
+                "Overlapped Comm-Comp": 0,
+                "Only Comm": 0,
+                "Other": 0,
+            }
+
+            i = 0
+            for rank in breakdown_dict:
+                for metric in breakdown_dict[rank]:
+                    mean_dict[metric] += breakdown_dict[rank][metric] / 1e9
+                i += 1
+
+            for key in mean_dict:
+                mean_dict[key] /= i
+
+            breakdown_dict["Average"] = mean_dict
+
+        self._match_events()
+
+        ranks = set(self.events["Rank"])
+
+        breakdown_dict = {str(p): {} for p in ranks}
+
+        process_labels_df = self.definitions.loc[
+            self.definitions["Definition Type"] == "process_labels"
+        ]
+        cpu_processes = set(
+            process_labels_df[
+                process_labels_df.apply(
+                    lambda row: row["Attributes"]["args"]["labels"].startswith("CPU"),
+                    axis=1,
+                )
+            ]["Process"]
+        )
+        filtered_df = self.events.loc[
+            (~self.events["Process"].isin(cpu_processes))
+            & (self.events["Process"].astype(str).str.isnumeric())
+        ]
+
+        for p in ranks:
+            gpu_df = filtered_df.loc[filtered_df["Rank"] == p]
+
+            gpu_df = gpu_df.loc[gpu_df["Event Type"] == "Enter"]
+
+            gpu_df["kernel_type"] = gpu_df.apply(
+                lambda row: kernel_type(row["Name"]),
+                axis=1,
+            )
+
+            comm_df = gpu_df.loc[gpu_df["kernel_type"] == "comm"]
+            comm_intervals = merge_intervals(
+                list(
+                    zip(
+                        comm_df["Timestamp (ns)"].to_list(),
+                        comm_df["_matching_timestamp"].to_list(),
+                    )
+                )
+            )
+
+            comp_df = gpu_df.loc[gpu_df["kernel_type"] == "comp"]
+            comp_intervals = merge_intervals(
+                list(
+                    zip(
+                        comp_df["Timestamp (ns)"].to_list(),
+                        comp_df["_matching_timestamp"].to_list(),
+                    )
+                )
+            )
+
+            comm_enter_times, comm_leave_times = zip(*comm_intervals)
+            comp_enter_times, comp_leave_times = zip(*comp_intervals)
+            comp_df = pd.DataFrame(
+                {
+                    "Timestamp (ns)": comp_enter_times,
+                    "_matching_timestamp": comp_leave_times,
+                }
+            )
+
+            total_comm_time, overlapped_comm_time = 0, 0
+            for j in range(len(comm_enter_times)):
+                curr_enter_time, curr_leave_time = (
+                    comm_enter_times[j],
+                    comm_leave_times[j],
+                )
+                total_comm_time += curr_leave_time - curr_enter_time
+
+                overlapped_df = comp_df.loc[
+                    (comp_df["Timestamp (ns)"] <= curr_leave_time)
+                    & (comp_df["_matching_timestamp"] >= curr_enter_time)
+                ]
+
+                overlapped_comm_time += overlapped_df.apply(
+                    lambda row: (
+                        min([row["_matching_timestamp"], curr_leave_time])
+                        - max([row["Timestamp (ns)"], curr_enter_time])
+                    ),
+                    axis=1,
+                ).sum()
+
+            breakdown_dict[str(p)]["Total Time"] = max(gpu_df["Timestamp (ns)"]) - min(
+                gpu_df["Timestamp (ns)"]
+            )
+            breakdown_dict[str(p)]["Non-overlapped Computation"] = (
+                comp_df["_matching_timestamp"] - comp_df["Timestamp (ns)"]
+            ).sum() - overlapped_comm_time
+            breakdown_dict[str(p)][
+                "Computation Overlapping w/ Communication"
+            ] = overlapped_comm_time
+            breakdown_dict[str(p)]["Non-overlapped Communication"] = (
+                total_comm_time - overlapped_comm_time
+            )
+            breakdown_dict[str(p)]["Other"] = breakdown_dict[str(p)]["Total Time"] - (
+                breakdown_dict[str(p)]["Non-overlapped Computation"]
+                + breakdown_dict[str(p)]["Non-overlapped Communication"]
+                + breakdown_dict[str(p)]["Computation Overlapping w/ Communication"]
+            )
+
+        mean_over_ranks(breakdown_dict)
+
+        return breakdown_dict