Ascend Communication Analysis

Goal

Diagnose Ascend collective communication cost from profiling artifacts. Always separate:

1. Wait-caused high duration: the communication op looks slow because this rank waits for another rank or an earlier compute/copy path.
2. Real communication bottleneck: transfer, link, scheduling, retry, relay, bandwidth, alignment, or contention is suspicious after wait is ruled out.

This skill focuses on collective communication. P2P analysis is not complete yet; if P2P appears, report it as a limitation and only summarize available evidence.

Data Scope

Current scope is intentionally limited to DB-based analysis. Use DB inputs in this priority order:

1. cluster_analysis_output/cluster_analysis.db
2. rank-level analysis.db
3. rank-level ascend_pytorch_profiler_{rank_id}.db
4. sibling profiler_info.json

Optional context:

• profiler_metadata.json: use for world_size, tensor_model_parallel_size, pipeline_model_parallel_size, data_parallel_size, and parallel_group_info

Do not treat JSON summary files, trace files, or non-DB artifacts as primary inputs in the current version of the skill. If none of the required DB files are present, state that the current skill scope does not cover the dataset yet.

If cluster-level analysis is requested but cluster_analysis_output/cluster_analysis.db is absent, first try to generate it by running:

msprof-analyze cluster -m all -d <cluster-data-path>

Here <cluster-data-path> is the cluster profiling dataset root provided by the user. After the command completes, re-check for cluster_analysis_output/cluster_analysis.db and continue with the DB-first workflow if generation succeeds.

Interpret the DB roles like this:

• cluster_analysis_output/cluster_analysis.db: first-priority source for cluster-wide communication diagnosis
• rank-level analysis.db: first-priority source for single-card communication diagnosis
• rank-level ascend_pytorch_profiler_{rank_id}.db: raw profiler DB used when communication task evidence is needed, especially for level1+

cluster_analysis.db Tables

Common cluster-analysis tables:

Table	Role	Use in this skill
CommunicationGroupMapping	Communication group/domain mapping table. It maps group hash / group_name to group metadata such as pg_name, group_id, group type, and rank_set.	First choice for resolving communication domains. Use it before treating ClusterCommunicationTime.group_name as a final domain label.
ClusterCommunicationTime	Cluster-wide communication large-op timing table. It records op name, group name, rank, step, elapsed time, wait time, transit time, and start timestamp.	Overview uses only op/domain/type/count and elapsed-time fields. Wait/transit fields are reserved for later deep-dive analysis.
ClusterCommunicationBandwidth	Cluster-wide communication bandwidth table. It records op name, group name, rank, step, link/band type, transit size, transit time, bandwidth, and large-packet ratio.	Use later only for selected candidate ops when checking real communication bottlenecks.
ClusterCommunicationMatrix	Cluster-wide communication matrix table. It records source/destination rank pairs, communication group, transport/link type, transit size/time, and bandwidth.	Use after candidate selection to inspect link-level route, rank-pair imbalance, local/global rank interpretation, and SDMA/RDMA evidence.

Collection Level Rules

Determine collection level from profiler_info.json, using its profiler_level-related field(s) as the source of truth.

• Level1 and above: communication small-task information is expected to exist in the DB, so COMMUNICATION_TASK_INFO-based analysis is allowed. This includes Notify Wait, small communication task decomposition, transfer size, transport details, peer rank fields, and richer wait evidence.
• Level0: COMMUNICATION_TASK_INFO can also exist and may be used for small-task names and timing evidence, including Notify Wait timing when present. However, level0 does not provide reliable transit_size, link_type / transport_type, src_rank, or dst_rank detail for small tasks; these fields may be filled with DB default values. Treat such default values as unknown, not measured facts. In level0, do not claim detailed transport breakdown, communication matrix evidence, or peer-rank direction from small-task fields unless a non-default value is explicitly present and corroborated by another DB source.
• Always report the profiler level in the final diagnosis.
• Prefer delivered aggregate DB fields such as transit_size, bandwidth, transit_time, and wait_time. Do not recompute them from inferred small-task relationships unless the DB already exposes the needed fields.

Concepts

• COMMUNICATION_OP: communication large op.
• COMMUNICATION_TASK_INFO: communication small task, including operations such as Notify Wait, Notify Record, Memcpy, Reduce Inline, RDMA send payload/notify, and related synchronization or transfer tasks. This table may exist even at profiler level0; at level0, size/link/peer columns can be DB defaults rather than collected values.
• LINK_TYPE / transport_type: transfer link type such as LOCAL, SDMA, RDMA, HCCS, or tool-specific labels.
• Communication op name pattern: {type}__{communication-domain-hash-suffix}_{index-in-domain}_{0/1}. Example: hcom_allGather__318_0_1.
• Rank IDs in rank-level matrix files can be communication-domain local ranks. Cluster analysis may map them to global ranks. Be explicit about local vs global rank evidence.

View communication in two independent layers:

Layer	Meaning	Evidence
Orchestration / expansion layer	where the communication algorithm is expanded and scheduled: HOST, HOST_ts, AI_CPU, or AIV	HCCL_OP_EXPANSION_MODE environment variable; Device type, communication operator types also matters
Data movement layer	which engine moves payload data: SDMA, RDMA, HCCS-related DMA path, or MTE data movement	link matrix, transit size/time, bandwidth, retry, relay

Do not collapse these layers into one "communication mode". For example, a communication op can be orchestrated by Host or AICPU while payload movement still uses SDMA or RDMA. Analyze orchestration overhead and data movement bottlenecks separately.

Orchestration / expansion modes:

Mode	Typical scene	Strength	Risk / constraint
HOST	default Host-side CPU expansion and dispatch	broadly supported and easy to reason about from Host timelines	Host launch/dispatch overhead can be visible for many small communication ops
HOST_ts	Host-side expansion with TS-assisted dispatch path when it appears in trace/tool labels	similar goal to AICPU for reducing Host-side issue overhead	naming and availability are tool/version dependent; verify from trace labels or environment evidence
AI_CPU / AICPU	Device-side AI CPU expansion and scheduling	saves Host dispatch time and is useful when Host launch overhead is material	AI CPU resource contention is possible; product/operator support and profiling constraints depend on CANN/product version
AIV	Device-side AI Vector Core expansion / execution path	low latency for supported small or medium communication patterns	consumes vector compute cores; support is constrained by product, operator, data type, and communication-domain concurrency

Data movement engines:

Mode	Typical scene	Strength	Risk
MTE	in-server small or medium communication, especially useful for MoE combine/dispatch; usually with AIV expansion mode.	low latency and high short-duration bandwidth	consumes vector compute cores
SDMA	in-server or cross-chip large data over HCCS	dedicated transfer engine, does not consume compute cores	higher startup latency than AIV
RDMA	cross-server communication	remote direct memory access	network/topology/congestion sensitive

Reference: HCCL_OP_EXPANSION_MODE configures communication algorithm expansion location, with documented values such as HOST, AI_CPU, and AIV: https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/81RC1beta1/maintenref/envvar/envref_07_0096.html

Reference bandwidth anchors from current expert notes:

Link/mode	Data size condition	Reasonable bandwidth
cross-chip SDMA memcpy	transit_size > 16 MB	about 19 GB/s
cross-chip SDMA inline-reduce	transit_size > 16 MB	about 17 GB/s
cross-chip/cross-node RDMA	transit_size > 1 MB	about 21 GB/s

Treat these as heuristics, not hard pass/fail rules. Small packets, overlap, topology, and measurement method can lower apparent bandwidth.

DB Access Policy

Use DB artifacts as the source of truth. For any SQL query or schema inspection, use msprof_mcp-execute_sql first.

Do not invoke the local sqlite3 CLI, Python sqlite3, or shell commands that open DB files for ad hoc querying when msprof_mcp-execute_sql is available. Only fall back to local DB querying if msprof_mcp-execute_sql is unavailable, cannot access the target DB, or fails on the required query.

Script Tools

Use bundled scripts for deterministic extraction when they cover the needed evidence. For fields not covered by scripts, use msprof_mcp-execute_sql; do not use local SQLite querying as the normal path. When cluster_analysis_output/cluster_analysis.db is missing but cluster-level analysis is needed, prefer invoking the official CLI directly before falling back to rank-level-only evidence:

msprof-analyze cluster -m all -d <profiling-root>

If command options, environment setup, or troubleshooting for msprof-analyze are needed, use the dedicated ascend-msprof-analyze-cli skill.

python3 skills/profiler/ascend-communication-analysis/scripts/<script>.py ...

Scripts:

Command	Purpose
summarize.py --db <cluster_analysis.db> --output-stats-csv <communication_stats.csv> --output-links-csv <group_link_overview.csv>	Summarize communication-group op time and group-level link statistics. The script owns its extraction logic and should be treated as the preferred overview path. It does not select deep-dive ops or diagnose faults.
collect_wait_evidence.py --db <cluster_analysis.db> --op-name <selected_op_name> [--op-name <selected_op_name> ...] --output-csv <timing_evidence.csv>	Collect cross-rank timing evidence for the selected communication op(s), including longest, shortest, earliest-start, and latest-start ranks. Use this as the primary table for wait diagnosis.

The script emits JSON to stdout by default and Markdown with --format md. Save only the CSV artifacts unless the user explicitly asks for a JSON file.

Workflow

During the workflow, surface key intermediate results to the user as soon as they are available. Print or summarize important findings from overview generation, candidate selection, wait gating, and transfer-evidence collection instead of keeping them only for the final report.

1. Locate required DB inputs: Search the user-provided path in this order: cluster-level cluster_analysis_output/cluster_analysis.db, rank-level analysis.db, rank-level ascend_pytorch_profiler_{rank_id}.db, and profiler_info.json.
   • If cluster-wide diagnosis is requested and cluster_analysis_output/cluster_analysis.db is missing, first run msprof-analyze cluster -m all -d <cluster-data-path> to generate it when the environment supports the CLI. Use the user-provided cluster dataset root as <cluster-data-path>, then re-scan for the generated DB before downgrading to rank-level evidence.
2. Choose analysis entry DB:
   • cluster communication: prefer cluster_analysis_output/cluster_analysis.db
   • single-card communication: prefer rank-level analysis.db
   • raw fallback / small-task evidence: use rank-level ascend_pytorch_profiler_{rank_id}.db
3. Read profiler level: Parse profiler_info.json and determine whether the dataset is level0 or level1+.
4. Set analysis mode:
   • level1+: allow large-op and small-task communication analysis, including size/link/peer fields when present
   • level0: allow large-op analysis and small-task timing/name analysis, but treat small-task transit_size, link_type / transport_type, src_rank, and dst_rank as unknown when they carry DB defaults
5. Build overview summaries: Use summarize.py when its input DB schema matches. The goal is to get two overview tables before selecting deep-dive candidates:
   • communication_stats: communication group + op type elapsed-time distribution. Use communication_stats to find high-cost groups/op types.
   • group_link_overview: communication group + link type transit size/time/bandwidth distribution from ClusterCommunicationMatrix. Use group_link_overview to see whether link time is concentrated in specific groups or transport types.
   • Prefer CommunicationGroupMapping to resolve communication groups. Do not assume ClusterCommunicationTime.group_name or op_suffix alone is the final label; it may be a group hash that must be mapped to pg_name, group_id, group type, or rank set.
   • Do not treat low min_bandwidth alone as a fault signal; it can be caused by small packets or local aggregate records.
   • If summarize.py fails or the schema differs, do not stop. Use msprof_mcp-execute_sql to inspect table and column names, then adapt the script's extraction intent into narrowly scoped queries that produce the same style of overview.
6. Select concrete communication instances for deep analysis:
   • Selects concrete high-cost collective communication instances from high-cost groups. In practice this usually means selecting a few concrete op_name values, optionally restricted to a target step.
   • Exclude aggregate rows such as Total Op Info; they are overview rows, not analyzable communication ops.
   • Report selection reasons directly in the final narrative.
7. Collect wait/timing evidence first: Use collect_wait_evidence.py only for the selected candidates.
   • Pass selected concrete op names through --op-name; repeat --op-name in the same command when multiple selected ops should be analyzed together.
   • Use --step-id when needed to limit the analysis to a specific step.
   • The agent decides from the evidence whether high duration is wait-caused, straggler-related, or insufficient evidence.

Example:

python3 skills/profiler/ascend-communication-analysis/scripts/collect_wait_evidence.py \
  --db cluster_analysis_output/cluster_analysis.db \
  --op-name hcom_allReduce__123_0_1 \
  --op-name hcom_allGather__318_0_1 \
  --op-name hcom_reduceScatter__456_0_1 \
  --output-csv timing_evidence.csv

8. Gate on wait diagnosis result: After Step 7, classify each selected communication instance as wait-caused, real-transfer-slow-suspected, or insufficient-evidence. If the evidence clearly shows the long duration is caused by inter-rank waiting, stop the diagnosis for that instance and do not continue into retry, relay, bandwidth, or fault-mode checks.
9. Collect transfer evidence only for non-wait cases: For each real-transfer-slow-suspected instance, choose the concrete communication op on the rank with the longest duration. Use msprof_mcp-execute_sql on ClusterCommunicationBandwidth, rank-level analysis.db, and when needed ClusterCommunicationMatrix to collect transfer size, transit time, bandwidth, link/band type, and rank-pair imbalance. Read the corresponding row in rank-level ascend_pytorch_profiler_{rank_id}.db COMMUNICATION_OP for relay and retry.
10. Compare peer and baseline behavior: For each transfer-slow case, compare the longest rank with peer ranks in the same communication instance, and compare against same communication type / similar transfer size / same communication group or link type when available. Optional checks include whether memory-intensive compute overlaps the same time window on that rank, whether a no-overlap same-shape communication instance provides a baseline, and whether the corresponding communication small tasks show abnormal decomposition or timing.
11. Produce evidence-backed gated diagnosis: For wait-caused instances, report only the wait chain and supporting timing fields. For real-transfer-slow-suspected instances, map the collected transfer evidence to the handbook fault modes when possible. For insufficient-evidence, state the missing DB tables, fields, or comparison samples. Every conclusion must cite the DB table / DB field / script output field that supports it, and separate overview facts from candidate-selection reasoning and later deep-dive conclusions.

Wait Diagnosis

Use two methods when possible:

Method	Evidence	Interpretation
Small-task Notify Wait	Large Notify Wait on a plane; src_rank / local_rank recorded in task args or columns	Valid as timing evidence when COMMUNICATION_TASK_INFO exists. Peer-rank interpretation requires non-default peer fields, usually level1+; at level0, report the wait timing but do not infer src_rank / dst_rank from DB defaults. Map local rank to global rank only when group mapping is available.
Cross-rank same-op alignment	Same op name/domain across ranks should finish close together; ranks that start early and spend longer may be waiting for ranks that start late or finish late	This method assumes the selected records are the same collective communication instance and that end-time differences for the same communication in this communication group are relatively stable. If end times are unstable or not aligned, do not infer wait from start/duration skew alone.

When reporting wait, name:

• waiting rank(s)
• suspected waited/late rank(s)
• wait duration or skew
• whether evidence uses global rank or local communication-domain rank
• confidence and missing evidence
• TP/PP/DP context from profiler_metadata.json
• the key ranks from timing_evidence.csv: longest_rank, shortest_rank, earliest_start_rank, and latest_start_rank

Fault-Mode Handbook

Hardware-related modes

Fault mode	Typical symptoms	Key evidence	How to distinguish it
Communication retry	Communication op duration is abnormally high, often with a single op or a few ops suddenly much slower than peer iterations; for large ops this often appears as communication-op duration above about 4 s.	In rank-level ascend_pytorch_profiler_{rank_id}.db, the corresponding row in COMMUNICATION_OP has a non-zero retry-like metric. The same op may also show poor bandwidth or elongated transfer time.	Retry is stronger than generic low bandwidth because it has an explicit retry counter. If hccn_tool -i <rank_id> -stat -g shows retry-related counters increased before vs after the job, that is direct supporting evidence. If all counters stay 0, do not claim historical retry on that rank.
Communication relay	Communication path is longer than expected and bandwidth may be lower than the normal direct path; some ranks may consistently look slower on the same route.	In rank-level ascend_pytorch_profiler_{rank_id}.db, the corresponding row in COMMUNICATION_OP has a non-zero relay-like metric. Matrix or link evidence may also indicate a non-direct route.	Prefer the explicit relay indicator over inferring from bandwidth alone. If hccn_tool -i <rank_id> -stat -g relay-related counters increased during the AI task, treat that as strong corroboration.
Communication congestion / backpressure	A large number of communication ops have non-small transfer size, but the measured bandwidth is persistently far below the heuristic anchors. The symptom may span multiple ops or ranks instead of a single isolated op.	Profiler-side evidence is: transfer size is not small, measured bandwidth is far below expectation, and stronger explanations such as retry, relay, or small-packet mode are absent. External evidence should come from hccn_tool -i <rank_id> -stat -g.	When many non-small communication ops all show abnormally low bandwidth, treat it as an important fault signal even before external confirmation. Still do not finalize congestion/backpressure from profiler alone; use hccn_tool counters before/after the job as stronger confirmation. If all returned counters are 0, that rank has no direct historical fault evidence from this tool.

Non-hardware modes