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Most GPU-cluster failures become tractable when you separate the CUDA API or kernel from NCCL collectives, the GPU-to-GPU or GPU-to-NIC path, and the underlying fabric. Record the complete software, hardware, topology, and rank context; reproduce first on one GPU, then one node, then multiple nodes; and change one variable at a time. This guide gives a repeatable workflow for CUDA faults, NCCL initialization and collective hangs, PCIe and GPUDirect RDMA problems, InfiniBand failures, and RoCE congestion.
A nonzero CUDA or NCCL return code is evidence at the API boundary, not an invitation to hide the error with retries. Preserve the first failing rank, host pair, command line, environment, and logs before attempting a fix.
Start with a failure record you can reproduce
Distributed jobs often produce a misleading final message: one rank reports an unhandled CUDA error while another waits in a collective, or a timeout appears after the original transport failure. Build a compact record before changing settings.
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- GPU model, count per host, and visible device order.
- GPU driver, CUDA toolkit/runtime, NCCL, framework, and container image versions.
- Host kernel, distribution, firmware where relevant, and container or virtual-machine runtime.
- NIC model, driver, link speed, and whether the path is Ethernet, InfiniBand, or RoCE.
- Node names, rank-to-host placement, CPU and NUMA placement, and the GPU/NIC/PCIe topology.
- Every relevant environment variable, including NCCL, CUDA, proxy, interface-selection, and scheduler variables.
- The exact launch command, world size, batch or tensor dimensions, and the first timestamped error from every rank.
Save command output rather than copying only the last screenful. A configuration mismatch between ranks can be more important than the apparent error text.
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Use a controlled reproduction ladder
- One GPU: run the smallest operation that fails. This distinguishes a kernel or runtime problem from distributed communication.
- Several GPUs on one node: exercise peer-to-peer and shared-memory paths without involving the fabric.
- One process per GPU on one node: test the framework’s rank setup and NCCL local transport.
- Two nodes and the smallest useful world size: test interface selection, RDMA or sockets, and rank-to-host reachability.
- Production scale: only after the reduced cases pass, restore the original placement, workload, and concurrency.
Retain logs at each level. If one GPU passes and two GPUs fail, investigate peer access, PCIe topology, shared memory, or local NCCL configuration before touching switches. If one node passes and two nodes fail, focus on interface selection, reachability, RDMA, and cross-host configuration.
Classify the symptom before changing configuration
| Symptom class | Typical first clue | First isolation target |
|---|---|---|
| CUDA runtime or kernel fault | Kernel launch error, illegal address, misaligned access, device-side assert, or a nonzero CUDA API result | The API boundary and the smallest single-GPU reproducer |
| NCCL initialization or collective failure | Ranks disagree on setup, communicator creation fails, or a collective times out | Rank configuration, selected interfaces/transports, shared memory, and NCCL diagnostics |
| GPU-to-GPU or GPU-to-NIC path issue | Peer access is unavailable, a pair is unusually slow, or GPUDirect RDMA fails | PCIe topology, IOMMU/ACS, NIC compatibility, and peer-memory path |
| Fabric or runtime problem | Multi-node hangs, link-level drops, congestion, or failures only in a container or VM | Independent network tests, counters, topology exposure, and runtime limits |
Do not infer the root cause from the last rank to print an error. CUDA operations are often asynchronous, so the first visible failure may be reported by a later API call or by a different rank.
Debug CUDA kernels and runtime errors
Check every CUDA return code
Wrap runtime and driver API calls so the operation, source location, and error string are recorded. Check launch status immediately, then synchronize at a deliberate boundary when debugging. A broad retry loop can erase the first useful error and leave later calls reporting a secondary failure.
# Illustrative CUDA error-checking pattern
kernel<<<grid, block, 0, stream>>>(args);
CUDA_CHECK(cudaGetLastError()); // launch/configuration error
CUDA_CHECK(cudaStreamSynchronize(stream)); // execution error at a known boundaryUse synchronization only for diagnosis or at well-defined correctness boundaries; removing unnecessary synchronization after the bug is understood avoids turning a debugging build into a permanently slower production path.
Minimize the reproducer
- Reduce tensor sizes, batch count, and the number of kernels while preserving the failure.
- Fix random seeds and record input shapes, strides, data types, and stream assignments.
- Run with one process and one GPU before involving a framework’s distributed launcher.
- Disable optional fusions or custom extensions one at a time to identify the operation that changes the result.
A minimal reproducer makes memory errors and races easier to attribute and lets you compare driver, toolkit, and container changes without the noise of a full job.
Use CUDA-GDB for control-flow and launch investigation
CUDA-GDB is appropriate when you need breakpoints, thread inspection, launch debugging, or to determine which kernel and source line first diverges. Build a debuggable binary with the symbols your build system supports, launch the smallest failing case under CUDA-GDB, and inspect the failing kernel rather than attaching it to an entire production-scale run.
Expect substantial runtime overhead and altered timing. A failure that disappears under a debugger is not disproved; record that behavior and move to a tool that perturbs execution differently.
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Use Compute Sanitizer for memory, race, and synchronization defects
Compute Sanitizer targets memory errors, data races, synchronization mistakes, initialization problems, and related launch defects. Run the smallest failing command first and keep the diagnostic output for the exact input and build.
compute-sanitizer --tool memcheck python train_one_step.py
compute-sanitizer --tool racecheck python test_kernel.py
compute-sanitizer --tool synccheck python test_kernel.py
compute-sanitizer --tool initcheck python test_kernel.pyTool names and supported options can vary with the installed CUDA release, so use the Compute Sanitizer documentation shipped for that release when an option is rejected. Treat the first reported invalid access or race as the primary lead; later failures may be consequences.
Recognize asynchronous-error traps
If a framework reports an “unhandled CUDA error” during an unrelated operation, insert a synchronization after the suspect launch and rerun. Check whether the error moves to that boundary. Also compare behavior with synchronous launch debugging enabled by the installed runtime, but keep that setting confined to the reproducer because it changes scheduling and performance.
Turn on NCCL diagnostics without drowning in logs
Collect controlled NCCL output
Start with NCCL’s built-in diagnostics and a small world size. Set logging for the failing run, capture each rank’s output separately, and include host and rank identifiers in the log filename.
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# Launch the same command on all ranks, retaining one log per rank.Use more verbose logging only after the initial output identifies a subsystem. Excessive debug output can change timing, exhaust log storage, and make the first divergent rank harder to see.
Compare all ranks, not just rank zero
- Confirm identical GPU driver, CUDA runtime, NCCL, framework, and container image versions.
- Compare visible-device mappings and rank-to-GPU assignments.
- Check that interface-selection and transport variables are identical where they should be, and intentionally different only where the launcher requires it.
- Compare hostname resolution, node names, and the addresses selected for communication.
- Check shared-memory size and permissions inside the runtime.
A communicator can fail because one rank selected a different interface, transport, or GPU than its peers even when the nodes appear healthy individually.
Inspect interfaces and transports deliberately
NCCL may choose sockets, InfiniBand/RDMA, shared memory, or GPU-direct paths depending on what it detects. Use interface-selection variables temporarily to test a hypothesis, not as permanent guesses. For example, selecting an explicit Ethernet interface or disabling RDMA for a comparison run can show whether the failure is transport-specific. Restore the known-good setting after the experiment and document the exact variable and value.
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# Examples for a controlled comparison; use names that exist on the host
export NCCL_SOCKET_IFNAME=^lo,docker
export NCCL_IB_HCA=mlx5_0
# Diagnostic comparison only; do not assume this is a production fix
export NCCL_IB_DISABLE=1Interface names and HCA names differ by host. A value that works on one node can silently select nothing on another, so verify the resulting NCCL log on every rank.
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NCCL diagnostic modes and test programs can exercise communication between selected GPUs and expose a failing pair, link, or transport. Run the smallest test that reproduces the symptom, then expand the matrix: same-node pairs, cross-socket pairs, and cross-node pairs. If only one GPU pair fails, stop changing global settings and inspect that pair’s PCIe path, peer access, and NIC association.
Verify GPU peer access and the PCIe path
Check topology visibility
Run topology and device queries on the host and inside the container or VM. Compare GPU-to-GPU and GPU-to-NIC relationships, PCI bus identifiers, NUMA nodes, and whether all expected devices are visible. A container that exposes GPUs but hides relevant PCI information can cause NCCL to choose a slower path or fail to establish a direct path.
nvidia-smi -L
nvidia-smi topo -m
nvidia-smi -q -d PCI
lspci -nn | grep -Ei '3D|VGA|Ethernet|InfiniBand'The exact topology output depends on hardware and driver support. Look for missing devices, unexpected host bridges, or a NIC that is not adjacent to the GPUs assigned to a rank.
Test peer-to-peer access explicitly
Use the CUDA samples or a framework diagnostic that reports peer access and bandwidth. A path can be functionally available but slower than expected when GPUs cross a socket or host bridge. If peer access is unavailable between a pair that should support it, inspect firmware, PCIe placement, virtualization settings, and isolation features before tuning NCCL.
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Investigate IOMMU and ACS
IOMMU mode and PCIe Access Control Services (ACS) can alter peer routing. In some topologies, forced upstream routing prevents direct GPU-to-GPU or GPU-to-NIC traffic and causes degraded performance or hangs. Check the host’s documented IOMMU and ACS configuration, then make one controlled change at a time during a maintenance window. Do not disable an isolation feature blindly: weigh the security and multi-tenant consequences, and confirm the platform vendor’s supported configuration.
Make GPUDirect RDMA prerequisites explicit
GPUDirect RDMA requires a compatible NIC and driver combination plus a supported peer-memory path. Depending on the software stack, that path uses the nvidia-peermem kernel module or a supported DMA-BUF integration.
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Check the module and driver state
lsmod | grep -E 'nvidia_peermem|nvidia-peermem'
modinfo nvidia-peermem 2>/dev/null
ibv_devinfo
rdma link showModule naming and availability vary by driver release. Verify that the module is loaded on every node that is expected to use GPUDirect RDMA, that the RDMA device is present, and that the NIC driver and firmware are compatible with the CUDA/NCCL stack.
Separate GPUDirect failure from ordinary RDMA failure
- Run an RDMA test with host memory if your test suite supports it.
- Run the corresponding GPU-buffer test.
- Compare bandwidth, completion errors, and whether only GPU buffers fail.
If host-memory RDMA works but GPU-buffer traffic fails, focus on peer-memory registration, module or DMA-BUF support, IOMMU/ACS, and container device permissions rather than the switch.
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Prove interface selection and reachability
Confirm that every node can resolve and reach the address NCCL is selecting. Check routing, firewall rules, MTU consistency, and whether the scheduler places ranks on networks that are intentionally isolated. A successful ping proves little about RDMA, but a failed route or wrong interface is sufficient to explain a communicator timeout.
ip -br addr
ip route
getent hosts node-b
ping -c 3 node-b
# For a selected TCP port, use a site-approved listener/client test.Use the same source interface and address family that the job uses. If the cluster has multiple fabrics, test each one separately and record the source address.
InfiniBand: use low-level bandwidth and link checks
Inspect link state, port counters, and subnet-manager health with the vendor-supported InfiniBand tools. Then run a low-level bandwidth or latency test, such as the perftest programs available on your system, between the same hosts and ports used by the job.
ibstat
ibdev2netdev
# Example perftest roles; use matching ports and options on both hosts
ib_write_bw --help
ib_write_lat --helpRun the server and client according to the installed perftest documentation; do not assume device names or port numbers. Compare a healthy host pair with the failing pair. Errors, renegotiated links, or markedly different results point to the fabric or host path before NCCL is involved.
RoCE: look beyond link-up status
RoCE can remain link-up while congestion, pause frames, ECN/CNP marking, or queue drops prevent useful progress. Inspect NIC statistics and switch telemetry for the affected ports.
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ethtool <iface>
ethtool -S <iface> | egrep -i 'drop|pause|pfc|ecn|cnp|buffer|error'
rdma link showCounter names are driver-specific. Correlate increases with the exact job window and host pair. Check that the intended priority flow control (PFC), explicit congestion notification (ECN), congestion-notification packets (CNP), MTU, and queue configuration are consistent end to end. A single mismatched switch or NIC setting can affect only particular traffic classes.
Use rping for an RDMA control-path check
rping can validate RDMA connection management and basic data exchange independently of NCCL. Run a server on one node and a client on the other using the selected RDMA device and address, following the installed tool’s help output. A failure here narrows the problem to RDMA addressing, permissions, drivers, or the fabric; a successful rping does not prove that GPUDirect GPU buffers or NCCL’s chosen transport are healthy.
Account for containers and virtual machines
Container checks
- Verify that all intended GPUs, NIC character devices, RDMA devices, and peer-memory interfaces are passed through.
- Compare
nvidia-smi topo -m, PCI visibility, and interface names inside and outside the container. - Check shared-memory allocation and IPC settings; a small container
/dev/shmcan break NCCL shared-memory transport. - Ensure the container’s user and security policy permit the required device, memlock, and RDMA operations.
- Use the same CUDA and NCCL libraries that the image was built and tested with; do not accidentally mix host and image libraries.
Virtual-machine checks
GPU and NIC passthrough, virtual PCIe topology, IOMMU configuration, and virtual-function capabilities determine whether peer access and GPUDirect paths are possible. Compare a bare-metal run with a VM run using the same reduced test. If the VM cannot expose the topology or peer-memory capability required by the path, forcing NCCL variables will not create it; use sockets or a supported virtualization configuration instead.
Compare diagnostic strategies before choosing one
| Comparison | Failure isolation | Observability | Performance impact | Reversibility |
|---|---|---|---|---|
| Single-node reproduction | Strong for CUDA, peer access, shared memory, and local NCCL | High; fewer ranks and paths | Low to moderate | High |
| Multi-node reproduction | Required for fabric, address-selection, and cross-host failures | Lower unless logs are per-rank and per-host | Moderate to high | High when world size is kept small |
| Socket transport | Separates basic reachability from RDMA-specific faults | Good TCP/interface logs | Often lower throughput and higher CPU use | High; change one NCCL variable |
| InfiniBand/RDMA transport | Tests the production fast path | Requires NIC, fabric, and NCCL logs together | Closest to production performance | High if tested on a small job |
| Bare metal | Exposes the full PCIe and device topology | Highest hardware visibility | Usually lowest diagnostic distortion | Moderate; scheduling may be limited |
| Container or VM | Reveals runtime exposure and permission issues | Can hide topology and counters | May add virtualization or IPC overhead | High if the image and passthrough settings are versioned |
| Passive configuration checks | Finds mismatches without generating traffic | Safe but may miss intermittent path faults | Minimal | Very high |
| Active path tests | Finds failing links, pairs, queues, and transports | Specific counters and bandwidth/latency results | Consumes fabric and can perturb production traffic | High when isolated to maintenance or test nodes |
Common failure patterns and targeted fixes
“Unhandled CUDA error” after a harmless-looking call
Insert an immediate launch check and a synchronization after the suspected kernel. Re-run under Compute Sanitizer, reduce the input, and inspect the first invalid access, race, or device-side assertion. Do not treat a later framework call as the origin merely because it printed the message.
NCCL initialization hangs before the first collective
Compare rank logs for the last completed initialization stage. Verify hostname and interface selection, open the required firewall path, check shared memory, and test a small socket-only comparison. If sockets pass and RDMA does not, inspect HCA selection, RDMA device state, GPUDirect prerequisites, and fabric counters.
Collective timeout with one rank missing
Find the rank and host that did not enter the collective. Check for an earlier CUDA fault, divergent control flow, process eviction, or a launcher mismatch. A collective requires matching calls and compatible tensor counts; increasing the timeout can conceal the cause without fixing it.
One GPU pair is slow or fails while others work
Map that pair’s PCI bus path, NUMA locality, peer-access result, and associated NIC. Look for a different host bridge, ACS/IOMMU routing, a disabled peer path, or a faulty link. Test the pair directly before changing cluster-wide NCCL settings.
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RoCE job stalls while links report up
Correlate ethtool -S counters with the job window. Investigate PFC pause and storm indicators, ECN/CNP marking, queue drops, MTU, and switch-to-NIC priority mapping. Validate with rping and a low-level RDMA test, then rerun the smallest NCCL case.
Only the container fails
Compare device nodes, PCI topology, RDMA visibility, /dev/shm, memlock, and library versions inside and outside the container. Fix the runtime exposure or image mismatch rather than applying arbitrary transport variables.
Change one variable, keep a rollback, and escalate with evidence
- Write down the current known-good or baseline environment and save its logs.
- Change exactly one item: an interface, transport, module, topology setting, container limit, or software version.
- Run the smallest reproducer at the same world size and placement.
- Compare the first failing rank, selected path, counters, and performance—not only whether the command exited.
- Revert the change if it does not isolate the fault, then test the next hypothesis.
For escalation, provide the minimal command, timestamps, rank-to-host map, GPU and NIC identifiers, driver/CUDA/NCCL/framework versions, container digest, kernel, relevant environment variables, topology output, NCCL logs, CUDA sanitizer output, and network or RDMA counters. Include whether the failure reproduces on one GPU, one node, and multiple nodes. This lets an administrator or vendor distinguish a deterministic software defect from a host-specific path or intermittent fabric problem.
Quick Recap
A compact decision checklist
- CUDA-only failure: check return codes, synchronize, minimize, then use CUDA-GDB or Compute Sanitizer.
- Local multi-GPU failure: inspect peer access, PCIe/NUMA topology, IOMMU/ACS, and shared memory.
- Cross-node NCCL failure: compare ranks and versions, inspect selected interfaces/transports, and run a socket comparison.
- RDMA-only failure: verify HCA and NIC drivers, peer-memory or DMA-BUF support, module state, and low-level RDMA tests.
- RoCE symptoms: inspect drops, pause, PFC, ECN/CNP, queues, and MTU rather than trusting link state alone.
- Container/VM-only failure: compare topology, device permissions, IPC/shared memory, libraries, and passthrough capabilities.
- Intermittent or pair-specific failure: preserve the exact host pair and rank, run active path tests, and avoid broad cluster-wide changes until the path is identified.
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