Teamcenter on Oracle AI Database@Azure: Architecture, Validation, and Results
Teamcenter on Azure with Oracle Exadata Database@Azure delivers seamless, low-latency connectivity between Teamcenter application tiers and Exadata within the same Azure region, combining Azure-native services with Oracle RAC-powered database capabilities. Successful end-to-end functional validation confirmed application compatibility, workload execution, backup and recovery, and stable database performance, demonstrating enterprise readiness for Teamcenter workloads.
Inside Llama 3.1 405B MLPerf Training on Azure: System-Level Insights at 8K+ GPU Scale
By Shantanu Patankar, and Azin Heidarshenas As part of Azure's MLPerf Training v6.0 submission, we scaled Llama 3.1 405B (NVFP4) pretraining to 8,192 GB200 GPUs across 128 racks on Azure's Fairwater infrastructure, converging in 7.07 minutes (MLPerf Training Blog). Llama 3.1, with 405 billion active parameters, has the highest active parameter count among all models in the MLPerf suite. Getting a model of this scale to train efficiently requires a cascade of tightly coupled choices around parallelism, batch sizing, and network placement. This post shares what we learned from bringing up and scaling Llama 3.1 405b on Fairwater at frontier scale. Three insights emerged: Even at 8K+ GPU scale, Llama 3.1 405B training remains compute-bound, with Feed forward Network (FFN) kernels dominating execution Topology-aware mapping is the key enabler of 8000+ GPU scale Scaling efficiency is governed by convergence dynamics, not system or network limits The rest of this post explains how these effects appear in practice. Where Does the Time Go Inside One Iteration, and What Are the Bottlenecks? Llama 3.1 405B is a dense transformer with 126 layers, each consisting of a self-attention block (QKV projections and attention) followed by a feed-forward network (FFN). To understand what limits performance at scale, we profiled steady-state iterations and broke down where time is spent, as shown in Figure 1. A single training step (Global Batch Size=1024) takes approximately 1.27s. 1: Compute vs Communication Breakdown of Llama 3.1 405B (Global Batch Size=1024) Compute: Roughly 57% of the training iteration wall time (~733 ms) is spent in compute. GEMM kernels dominate, accounting for 74% of that compute time. What is striking is how concentrated the compute is. The FFN up-projection and down-projection GEMMs alone consume ~54% of total compute (~397 ms). Everything else, including QKV projections, attention, softmax, normalization, etc., shares the remaining 46%. the FFN clearly dominates and is the primary driver of compute time. Communication: The system operates across two communication domains: high-bandwidth NVIDIA NVLink (NVL) within a rack (scale-up, 72 GPUs) and Azure’s Multipath Reliable Connection (MRC) network across racks (scale-out). The total communication work is 853 GPU-ms. Nearly 42% of this overlaps with compute or other communication (with MRC overlapping NVL), leaving only ~500 ms on the critical path. This is the portion that directly impacts step time, as shown in Figure 1. Of this ~500 ms critical-path communication, NVLink accounts for nearly all of it, while MRC contributes just 1.6%. Intra-rack NVLink communication on the critical path accounts for 37.8% (~479 ms). This includes Tensor Parallelism (TP), Pipeline Parallelism (PP), and Context Parallelism (CP) exchanges on the 1.8 TB/s intra-rack NVLink fabric, as well as pipeline bubbles, where stages idle between micro-batches. Although substantial, in a weak scaling regime, where each rack handles a fixed shard of the workload, NVLink communication per rack remains constant as we scale. MRC communication covers gradient synchronization across all DP ranks. Although it represents a substantial amount of communication work, it overlaps heavily with both NVLink communication and compute. As a result, only 1.6% (~20 ms) remains on the critical path. The cross-rack MRC network is nearly invisible in the step profile. The workload is compute-bound even at 8192 GPU scale, with FFN as the center of gravity. Any improvement to FFN kernel efficiency (precision, tiling, scheduling) translates almost directly into faster training steps. Why Is Topology-Aware Mapping the Key Enabler of Scale? In the first section, we showed that cross-rack MRC communication accounts for just 1.6% of step time. This result comes from how parallelism is mapped to the cluster’s two-tier network. Training Llama 3.1 405B distributes work across four dimensions. Tensor Parallelism (TP), Pipeline Parallelism (PP), and Context Parallelism (CP) split the model and sequence across GPUs, communicating frequently throughout each step. Data Parallelism (DP) replicates the model and synchronizes gradients once per step. The key difference: TP, PP, and CP are latency-sensitive. DP communication can be overlapped with compute, along with other NVLink-based communication. The design principle follows directly: keep latency-sensitive dimensions on the fastest links, and push traffic that can be overlapped with compute to the scale-out fabric On Azure's Fairwater cluster, each rack provides a 72-GPU NVLink domain at 1.8 TB/s. We use 64 GPUs per rack and constrain TP × PP × CP = 64, so one complete model-parallel shard fits within a single rack. Scaling to more racks adds DP replicas, with gradient synchronization flowing across the 100 GB/s Multipath Reliable Connection (MRC) network, the traffic that hides almost entirely behind compute. We swept TP, PP, and CP at fixed DP = 8 and GBS = 1152 to isolate the effect of communication efficiency on throughput. TP4–PP8–CP2 achieves the highest TFLOPS/GPU among the feasible configurations, reaching an Model FLOPs Utilization (MFU) of 26.45% as shown in Figure 2. What Limits Scaling Efficiency at 8K+ GPU Scale? We scaled Llama 3.1 405B training from 40 to 128 racks, adjusting MiniBS (per-DP-shard batch size, i.e., Global Batch Size ÷ DP) and Global Batch Size (GBS) at each scale point to balance GPU utilization against convergence efficiency. Training time drops from 18.79 minutes to 7.07 minutes, representing 83% scaling efficiency relative to the 40-rack baseline as shown in the table below. Racks GPUs MiniBS (GBS/Racks) Global Batch Size (GBS) Time-to-Train 40 2,560 24 960 18.79 min 64 4,096 16 1024 12.86 min 112 7,168 8 896 7.75 min 128 8,192 8 1,024 7.07 min Notice that MiniBS shrinks as we scale. MiniBS determines how much work each GPU processes per step and is the primary driver of step time. As we scale by increasing DP (one replica per rack), the global batch size grows proportionally unless MiniBS is reduced. Higher MiniBS improves per-step GPU utilization, but at scale it inflates GBS, and beyond a critical batch size the model requires quadratically more samples to converge. So we trade per-step throughput for faster end-to-end convergence, reducing MiniBS from 144 at 8 racks to 8 at 128 racks. MiniBS cannot be reduced below 8 in our setup because PP = 8 requires at least one microbatch per pipeline stage to keep the pipeline fully utilized. Since MiniBS cannot drop further, adding more racks only increases GBS, pushing deeper into the convergence penalty. At large scale, the optimal configuration minimizes time-to-train, not per-step throughput. The 83% scaling efficiency gap is not a network or system bottleneck. It is inherent to the benchmark's convergence dynamics. Conclusion MLPerf Training v6.0 provides a practical lens into how frontier-scale LLM systems behave under real constraints. Azure’s Llama 3.1 405B submission, which achieved a leading result at 8K+ GPU scale, highlights how performance at this level is shaped not just by infrastructure, but by the interaction of compute, topology, and convergence. Scaling efficiently at this level requires aligning these factors with the workload, not just adding more compute. View Azure's MLPerf Training v6.0 results. Read Azure's MLPerf v6.0 announcement blog. Acknowledgement This work was made possible through strong collaboration across multiple teams. We thank Prabhat Ram for his guidance throughout this effort, along with Mark Gitau, Amirreza Rastegari, Ojasvi Bhalerao, Sai Kovouri, Adam Hough, Bhupender Thakur, Nandini Ramanathan, Manasa Govindu, Sanian Gaffar, Ekrem Aksoy, Matthew Kappel, John Rankin, Girish Bhatia, Sreevatsa Anantharamu, Scott Moe, Yang Wang, and Jithin Jose — as well as many others across Azure and NVIDIA who supported this effort.Scaling High-Performance CAD on Azure Virtual Desktop with NVIDIA RTX PRO 6000
This blog presents a validation study of Siemens NX 2506 running in a multi-user Azure Virtual Desktop (AVD) environment powered by NVIDIA RTX PRO 6000 GPUs. It demonstrates how a single GPU-backed Azure VM can support up to 30 concurrent CAD users with stable rendering, consistent performance, and successful execution of all ATS benchmark workloads. The study also highlights performance improvements achieved through multi-host scaling, making Azure AVD a compelling platform for high-density, enterprise-scale CAD deployments.
Azure Sets a New Performance Record for LLM Training Benchmark at Extreme Scale
Azure achieved the most performant MLPerf Training v6.0 result to date for Llama 3.1 405B, with a time-to-train of just over seven minutes according to MLCommons. This loadbearing benchmark measures how communication overhead and system stability dominate training performance, where Azure’s full stack, end-to-end advantages shine. Azure scaled to 2,048 NVIDIA GB200 NVL72 compute tray nodes spanning 128 racks (8,192 GPUs), assembling the largest reported GB200 NVL72 cluster to date in MLPerf Training. As model sizes continue to grow into hundreds of billions of parameters, achieving this level of performance consistently requires not only massive compute capacity, but also a highly efficient communication infrastructure. To meet the demands of the massive training workloads, Azure’s Fairwater AI supercomputing infrastructure offers high-performance GPU scale-up domains with resilient, scale-out networking. Fairwater is optimized for frontier-scale distributed AI training, where communication overhead and synchronization latency can become major scaling bottlenecks at multi-thousand GPU scale. This remarkable result was possible by leveraging fifth-generation NVIDIA NVLink at 1,800 GB/s per GPU for intra-rack communication and Azure’s 100 GB/s MRC (Multipath Reliable Connection) fabric accelerated by NVIDIA DOCA and connected with NVIDIA ConnectX-8 and NVIDIA Spectrum-X Ethernet switches for inter-rack communication. What Enabled Scaling Llama 405B to 8,192 GPUs on Azure Achieving efficient training at this scale requires more than raw compute. Three architectural ingredients came together to make this result possible: High operational efficiency of NVLink scale-up domains, providing fast intra-rack GPU-to-GPU communication. Resiliency and stability of Azure’s MRC scale-out networking fabric, built on NVIDIA ConnectX-8 SuperNICs and Spectrum-X Ethernet switches, delivering 100 GB/s per GPU across racks. Topology-aware workload mapping, aligning parallelism strategies with the underlying network structure. Large-scale LLM training is fundamentally synchronous. At every training step, GPUs perform forward and backward passes followed by gradient synchronization across ranks. Because all GPUs must stay in sync, training progress is ultimately limited by the slowest rank — any time spent waiting on the network directly increases overall step time. For the Llama 405B benchmark, the workload is distributed across four dimensions of parallelism: Tensor, Context, Pipeline, and Data parallelism. Each generates different communication patterns, with some communication sitting directly on the critical training path. If left unmanaged, communication overhead can quickly become a major bottleneck at scale. The key insight is that not all communication is equally latency-sensitive. Some communication must be completed before computation can continue, while other communication can overlap with compute. Tensor-Parallel and Context-Parallel communication sit directly on the critical compute path, meaning each layer must wait for its collective operation to complete before execution can proceed. Pipeline-Parallel communication is also on the critical path, but across stages rather than within a layer (i.e., the next stage cannot begin processing its data until the previous stage has transferred its gradients). To minimize latency, all three are placed on the high-bandwidth NVLink domain, which provides up to 1,800 GB/s per GPU. Data-Parallel communication, on the other hand, can overlap with backward compute because gradient reduction runs alongside the backward pass and does not immediately block the next operation. This traffic is carried over the MRC scale-out network. The impact of such mapping is clear at scale. Our profiling shows that cross-rack MRC communication contributes just ~20 ms of exposed time on the critical path of a ~1.27 s training step (≈1.6% of step time). This communication — primarily gradient synchronization across all ranks — is well overlapped by compute and NVLink traffic, making the scale-out network nearly invisible to training performance. Stable Step Time as the Cluster Grows To evaluate execution stability as the cluster scales, we compared our GB200 NVL72 128-rack submission against a 112-rack configuration used for this experiment. In the Llama 405B workload, the critical path of each training step is dominated by compute (forward and backward GEMMs) together with latency-sensitive Tensor-Parallel and Context-Parallel communication over NVLink. Importantly, neither of these changes as the cluster scales out. Cross-node communication, such as Data-Parallel collectives and Pipeline-Parallel transfers over the MRC network, runs concurrently with backward compute and remains largely hidden behind computation. This overlap is sustained only if cross-node communication remains fast and predictable. Any network instability, congestion, or synchronization jitter would reduce this overlap and expose communication on the critical path, directly increasing overall step time. Figure 1. Step-time vs cluster size at scale This is exactly what we observe in practice under a weak scaling model, where we maintain a constant workload per GPU while scaling total cluster capacity. As shown in Figure 1, step time remains nearly identical at scale, measuring 1.2734 seconds at 112 GB200 NVL72 racks (7,168 GPUs) and 1.2712 seconds at 128 racks (8,192 GPUs) — a difference of just 2 ms. This corresponds to a near-perfect 99.8% weak scaling efficiency as we expanded the cluster by an additional 1,024 GPUs. Step-time variance also remains extremely low (±0.04–0.05%), confirming stable and predictable execution at extreme scale. This directly translates to maximized hardware utilization and an accelerated overall time-to-train, demonstrating the resiliency of Azure’s MRC scale-out network. Acknowledgement This work was made possible through strong collaboration across multiple teams. We would like to especially recognize Shantanu Patankar for his contributions to execution and performance analysis and Mark Gitau for his contributions to data preparation and experiment support as core contributors, and extend our thanks to Amirreza Rastegari, Ojasvi Bhalerao, Sai Kovouri, Adam Hough, Nandini Ramanathan, Manasa Govindu, Sanian Gaffar, Ekrem Aksoy, Bhupender Thakur, Matthew Kappel, John Rankin, Girish Bhatia, Sreevatsa Anantharamu, Scott Moe, Yang Wang, and Jithin Jose, as well as many others across Azure and NVIDIA who supported this effort.Azure Recognized as an NVIDIA Cloud Exemplar, Setting the Bar for AI Performance in the Cloud
As AI models continue to scale in size and complexity, cloud infrastructure must deliver more than theoretical peak performance. What matters in practice is reliable, end-to-end, workload-level AI performance—where compute, networking, system software, and optimization work together to deliver predictable, repeatable results at scale. This directly translates to business value: efficient full-stack infrastructure accelerates time-to-market, maximizes ROI on GPU and cloud investments, and enables organizations to scale AI from proof-of-concept to revenue-generating products with predictable economics. Today, Microsoft is proud to share an important milestone in partnership with NVIDIA: Azure has been validated as an NVIDIA Exemplar Cloud, becoming the first cloud provider recognized for Exemplar-class AI performance aligned with GB300-class (Blackwell generation) systems. This recognition builds on Azure’s previously validated Exemplar status for H100 training workloads and reflects NVIDIA’s confidence in Azure’s ability to extend that rigor and performance discipline into the next generation of AI platforms. What Is NVIDIA Exemplar Cloud? The NVIDIA Exemplar Cloud initiative celebrates cloud platforms that demonstrate robust end-to-end AI workload performance using NVIDIA’s Performance Benchmarking suite. Rather than relying on synthetic microbenchmarks, Performance Benchmarking evaluates real AI training workloads using: Large-scale LLM training scenarios Production-grade software stacks Optimized system and network configurations Workload-centric metrics such as throughput and time-to-train Achieving Exemplar validation signals that a provider can consistently deliver world-class AI performance in the cloud, showcasing that end users are getting optimal performance value by default. Proven Exemplar Validation on H100 Azure’s Exemplar Cloud journey began with publicly shared benchmarking results for H100-based training workloads, where Azure ND GPU clusters demonstrated exemplar performance using NVIDIA Performance Benchmarking recipes. Those results—published previously and validated through NVIDIA’s benchmarking framework—established a proven foundation of end-to-end AI performance for large-scale, production workloads running on Azure today. Extending Exemplar-Class AI Performance to GB300-Class Platforms Building on the rigor and learnings from H100 validation, Microsoft has now been recognized by NVIDIA as the first cloud provider to achieve Exemplar-class performance and readiness aligned with GB300-class systems. This designation reflects NVIDIA’s assessment that the same principles applied to H100—including end-to-end system tuning, networking optimization, and software alignment—are being successfully carried forward into the Blackwell generation. Rather than treating GB300 as a point solution, Azure approaches it as a continuation of a proven performance model: delivering consistent world-class AI performance in the cloud while preserving the flexibility, elasticity, and global scale customers expect. What Enables Exemplar-Class AI Performance on Azure Delivering Exemplar-class AI performance requires optimization across the full AI stack: Infrastructure and Networking High-performance Azure ND GPU clusters with NVIDIA InfiniBand NUMA-aware CPU, GPU, and NIC alignment to minimize latency Tuned NCCL communication paths for efficient multi-GPU scaling Software and System Optimization Tight integration with NVIDIA software, including Performance Benchmarking recipes and NVIDIA AI Enterprise Parallelism strategies aligned with large-scale LLM training Continuous tuning as models, workloads, and system architectures evolve End-to-End Workload Focus Measuring real training performance, not isolated component metrics Driving repeatable improvements in application-level throughput and efficiency Closing the performance gap between cloud and on-premises systems—without sacrificing manageability Together, these capabilities enabled Azure to deliver consistent Exemplar-class AI performance across generations of NVIDIA platforms. What This Means for Customers For customers training and deploying advanced AI models, this milestone delivers clear benefits: World-class AI performance in a fully managed cloud environment Predictable scaling from small clusters to thousands of GPUs Faster time to train and improved performance per dollar Confidence that Azure is ready for Blackwell-class and GB300-class AI workloads As AI workloads become more complex and reasoning-heavy, infrastructure performance increasingly determines outcomes. Azure’s NVIDIA Cloud Exemplar recognition provides a clear signal: customers can build and scale next-generation AI systems on Azure without compromising on performance. Learn More DGX Cloud Benchmarking on Azure DGX Cloud Benchmarking on Azure | Microsoft Community Hub
Centralized cluster performance metrics with ReFrame HPC and Azure Log Analytics
Imagine having several clusters across different environments (dev, test and prod) or planning a migration between PBS and Slurm or porting codes to a different system. They can all seem like daunting tasks. This is where the combination of ReFrame HPC, a powerful and feature rich testing framework, and Azure Log Analytics can help improve confidence and assurance in the performance and accuracy of a system. Here we will look at how to configure ReFrame HPC specifically for Azure: Deploying the required Azure resources, running a test and capturing the results in Log Analytics for analysis. Deploying the required Azure Resources Firstly, deploy the required resources in Azure by using this bicep from GitHub. The deployment includes the creation and configuration of everything required for ReFrame HPC. These resources include a data collection endpoint, a data collection rule and a log analytics workspace. Running ior via ReFrame HPC For the purpose of demonstrating a running test and capturing the results in Azure from start to finish, here is a simple ior test which will run both a read and a write operation against the shared storage. import reframe as rfm import reframe.utility.sanity as sn @rfm.simple_test class SimplePerfTest(rfm.RunOnlyRegressionTest): valid_systems = ["*"] valid_prog_environs = ["+ior"] executable = 'ior' executable_opts = [ '-a POSIX -w -r -C -e -g -F -b 2M -t 2M -s 25600 -o /data/demo/test.bin -D 300' ] reference = { 'tst:hbv4': { 'write_bandwidth_mib': (500, -0.05, 0.1, 'MiB/s'), 'read_bandwidth_mib': (350, -0.05, 0.5, 'MiB/s'), } } @sanity_function def validate_run(self): return sn.assert_found(r'Summary of all tests:', self.stdout) @performance_function('MiB/s') def write_bandwidth_mib(self): return sn.extractsingle(r'^write\s+([0-9]+\.?[0-9]*)', self.stdout, 1, float) @performance_function('MiB/s') def read_bandwidth_mib(self): return sn.extractsingle(r'^read\s+([0-9]+\.?[0-9]*)', self.stdout, 1, float) Test explanation Set the binary to be executed to ior, along with its arguments. executable = 'ior' executable_opts = [ '-a POSIX -w -r -C -e -g -F -b 2M -t 2M -s 25600 -o /data/demo/test.bin -D 300' ] Specify which systems the test should run on. In this case, any system/cluster which is known to have ior available will be selected. Look at the ReFrame HPC documentation to get a better understanding of the options available for use. valid_systems = ["*"] valid_prog_environs = ["+ior"] Verify the stdout of the job by searching for a specific value to assert that it ran successfully. @sanity_function def validate_run(self): return sn.assert_found(r'Summary of all tests:', self.stdout) If the sanity function passed it will then extract the performance metrics from the stdout of the job. The naming of the methods is important, as they will be stored in the results later. @performance_function('MiB/s') def write_bandwidth_mib(self): return sn.extractsingle(r'^write\s+([0-9]+\.?[0-9]*)', self.stdout, 1, float) @performance_function('MiB/s') def read_bandwidth_mib(self): return sn.extractsingle(r'^read\s+([0-9]+\.?[0-9]*)', self.stdout, 1, float) Performance references are used to determine if the current cluster has met the requirement or not. It also allows margins to be specified in either direction. reference = { 'tst:hbv4': { 'write_bandwidth_mib': (500, -0.05, 0.1, 'MiB/s'), 'read_bandwidth_mib': (350, -0.05, 0.5, 'MiB/s'), } } ReFrame HPC Configuration The ReFrame HPC configuration is key to determine how and where the test will run. It is also where the logic allowing Reframe HPC to use Azure for centralized logging will be defined. The full configuration file is vast and is covered in detail within the ReFrame HPC documentation. For the purpose of this test an example can be found on GitHub. Below is a breakdown of the key parts that allow Reframe HPC to push its results into Azure Log Analytics. Logging Handler The most important part of this configuration is the logging section, without it ReFrame HPC will not attempt to log the results. A handler_perflog of type httpjson is added to enable the logs to be sent to a HTTP endpoint with specific values which our covered below. 'logging': [ { 'perflog_multiline': True, 'handlers_perflog': [ { 'type': 'httpjson', 'url': 'REDACTED', 'level': 'info', 'debug': False, 'extra_headers': {'Authorization': f'Bearer {_get_token()}'}, 'extras': { 'TimeGenerated': f'{datetime.now(timezone.utc).isoformat()}', 'facility': 'reframe', 'reframe_azure_data_version': '1.0', }, 'ignore_keys': ['check_perfvalues'], 'json_formatter': _format_record } ] } Multiline Perflog To ensure this works with Azure, enable perflog_multiline. This will ensure a single record per metric is sent to Log Analytics. This is the cleanest way to output the results. Having this set to False will move the metric names into column names, which means that the schema will be different for each test and will become hard to maintain. Extra Headers A bearer token is required to authenticate the request. ReFrame HPC allows the adding of headers via the extra_headers property and a simple Python function, which obtains a scoped token that can be appended to the additional header. def _get_token(scope='https://monitor.azure.com/.default') -> str: credential = DefaultAzureCredential() token = credential.get_token(scope) return token.token Url Structure The url can be found in the output of the bicep which was run previously. It can also be obtained via the portal. Here is the structure of the url for reference. '${dce.properties.logsIngestion.endpoint}/dataCollectionRules/${dcr.properties.immutableId}/streams/Custom-${table.name}?api-version=2023-01-01' json Formatter A small work around is needed as the Data Collection Rule expects an array of items and ReFrame HPC outputs a single record. To resolve this another Python function can be used which simply wraps the record up in an array. In this example it also tidys up and removes some items that are not required and would cause issues with the json serialization. def _format_record(record, extras, ignore_keys): data = {} for attr, val in record.__dict__.items(): if attr in ignore_keys or attr.startswith('_'): continue data[attr] = val data.update(extras) return json.dumps([data]) Running the Test Now that the infrastructure has been deployed, the test has been defined and is correctly configured, we can run the test. Start by logging in. Here I am using the managed identity of the node, but User auth and User Assigned Managed Identities are also supported. $ az login --identity ReFrame HPC can be installed via Spack or Python and, while I am using Spack for packages on the cluster, I find the simplest approach is to activate a Python environment and install ReFrame HPC along with test specfic Python dependencies. $ python3 -m venv .venv $ . .venv/bin/activate $ python -m pip install -U pip $ pip install -r requirements.txt Now using the ReFrame HPC cli, the test can be run using the configuration file and the test file. $ reframe -C config.py -c simple_perf.py --performance-report -r ReFrame HPC will now run the test against the system/cluster defined in the configuration. For this example it is a Slurm cluster on a partition of HBv4 nodes and running squeue clarifys that. $ squeue JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) 955 hbv4 rfm_Simp jim.pain R 0:28 1 tst4-hbv4-97 Results And there we have it, results are now appearing in Azure! From here we can use kql to query and filter the results. This is just a subset of the values available but the dataset is vast and includes a huge range of values that are extremely helpful. Summary By standardizing on the combination of ReFrame HPC and Azure Log Analytics for testing and reporting of performance data across our clusters, whether Slurm based, Azure CycleCloud or existing on-prem clusters, you can gain unprecendented visibility and confidence in the systems you manage and the codes you deploy that were previously hard to obtain. Enabling the potential for: 🔎Fast cross-cluster comparisions 📈Trend analysis over long running periods 📊Standardized metrics regardless of scheduler or system ☁️Unified monitoring and reporting across clusters ReFrame HPC is suitable for a wide range of testing, so if testing is something you have been looking to implement, take a look at ReFrame HPCScaling physics-based digital twins: Neural Concept on Azure delivers a New Record in Industrial AI
Automotive Design and the DrivAerNet++ Benchmark In automotive design, external aerodynamics have a direct impact on performance, energy efficiency, and development cost. Even small reductions in drag can translate into significant fuel savings or extended EV range. As development timelines accelerate, engineering teams increasingly rely on data-driven methods to augment or replace traditional CFD workflows. MIT’s DrivAerNet++ dataset is the largest open multimodal dataset for automotive aerodynamics, offering a large-scale benchmark for evaluating learning-based approaches that capture the physical signals required by engineers. It includes 8,000 vehicle geometries across 3 variants (fastback, notchback and estate-back) and aggregates 39 TB of high-fidelity CFD outputs such as surface pressure, wall shear stress, volumetric flow fields, and drag coefficients. Benchmark Highlights Neural Concept trained its geometry-native Geometric Regressor, designed to handle any type of engineering data. The benchmark was executed on Azure HPC infrastructure to evaluate the capabilities of the geometry-native platform under transparent, scalable, and fully reproducible conditions. Surface pressure: Lowest prediction error recorded on the benchmark, revealing where high- and low-pressure zones form. Wall shear stress: Outperforming all competing methods to detect flow attachment and separation for drag and stability control. Volumetric velocity field: More than 50% lower error than previous best, capturing full flow structure for wake stability analysis. Drag coefficient Cd: R² of 0.978 on the test set, accurate enough for early design screening without full CFD runs. Dataset Scale and Ingestion: 39 TB of data was ingested into Neural Concept’s platform through a parallel conversion task with 128 workers and 5 GB RAM each that finished in about 1 hour and produced a compact 3 TB dataset in the platform’s native format. Data Pre Processing: Pre-processing the dataset required both large-scale parallelization and the application of our domain-specific best practices for handling external aerodynamics workflows. Model Training and Deployment: Training completed in 24 hours on 4 A100 GPUs, with the best model obtained after 16 hours. The final model is compact and real-time predictions can be served on a single 16 GB GPU for industrial use. Neural Concept outperformed all other competing methods, achieving state-of-the-art performance prediction on all metrics and physical quantities within a week: “Neural Concept’s breakthrough demonstrates the power of combining advanced AI with the scalability of Microsoft Azure,” said Jack Kabat, Partner, Azure HPC and AI Infrastructure Products, Microsoft. “By running training and deployment on Azure’s high-performance infrastructure — specifically the NC A100 Virtual Machine— Neural Concept was able to transform 39 terabytes of data into a production-ready workflow in just one week. This shows how Azure accelerates innovation and helps automotive manufacturers bring better products to market faster.” For additional benchmark metrics and comparisons, please refer to the Detailed Quantitative Results section at the end of the article. From State-Of-The-Art Benchmark Accuracy to Proven Industrial Impact Model accuracy alone is necessary, but not sufficient for industrial impact. Transformative gains at scale and over time are only revealed once high-performing models are deployed into maintainable and repeatable workflows across organizations. Customers using Neural Concept’s platform have achieved: 30% shorter design cycles $20M in savings on a 100,000-unit vehicle program These outcomes fundamentally result from a transformed, systematic approach to design, unlocking better and faster data-driven decisions. The Design Lab interface, described in the next section, is at the core of this transformation. Within Neural Concept’s ecosystem, validated geometry and physics models can be deployed directly into the Design Lab - a collaborative environment where aerodynamicists and designers evaluate concepts in real time. AI copilots provide instant performance feedback, geometry-aware improvement suggestions, and live KPI updates, effectively reconnecting aerodynamic analysis with the pace of modern vehicle design. CES 2026: See how OEMs are transforming product development with Engineering Intelligence Neural Concept and Microsoft will showcase how AI-native aerodynamic workflows can reshape vehicle development — from real-time design exploration to enterprise-scale deployment. Visit the Microsoft booth to see DrivAerNet++ running on Azure HPC and meet the teams shaping the future of automotive engineering. Visit Microsoft Booth to find out more Neural Concept’s executive team will also be at CES to share flagship results achieved by leading OEMs and Tier-1 suppliers already using the platform in production. Learn more on: https://www.neuralconcept.com/ces-2026 Credits Microsoft: Hugo Meiland (Principal Program Manager), Guy Bursell (Director Business Strategy, Manufacturing), Fernando Aznar Cornejo (Product Marketing Manager) and Dr. Lukasz Miroslaw (Sr. Industry Advisor) Neural Concept: Theophile Allard (CTO), Benoit Guillard (Senior ML Research Scientist), Alexander Gorgin (Product Marketing Engineer), Konstantinos Samaras-Tsakiris (Software Engineer) Detailed Quantitative Results In the sections that follow, we share the results obtained by applying Neural Concept’s aerodynamics predictive model training template to Drivaernet++. We evaluated our model’s prediction errors using the official train/test split and the standard evaluation strategy. For comparison, metrics from other methods were taken from the public leaderboard. We reported both Mean Squared Error (MSE) and Mean Absolute Error (MAE) to quantify prediction accuracy. Lower values for either metric indicate closer agreement with the ground truth simulations, meaning better predictions. 1. Surface Field Predictions: Pressure and Wall Shear Stress We began by evaluating predictions for the two physical quantities defined on the vehicle surface. Surface Pressure The Geometric Regressor achieved substantially better performance than all existing methods in predicting surface pressure distribution. Rank Deep Learning Model MSE (*10-2, lower = better) MAE (*10-1, lower = better) #1 Neural Concept 3.98 1.08 #2 GAOT (May 2025) 4.94 1.10 #3 FIGConvNet (February 2025) 4.99 1.22 #4 TripNet (March 2025) 5.14 1.25 #5 RegDGCNN (June 2024) 8.29 1.61 Table 1: Neural Concept’s Geometric Regressor predicts surface pressure more accurately than previously published state-of-the-art methods. The dates indicate when the competing model architectures were published. Figure 1: Side-by-side comparison of the ground truth pressure field (left), Neural Concept model’s prediction (middle), and the corresponding error for a representative test sample (right). Wall Shear Stress Similarly, the model delivered top-tier results, outperforming all competing methods. Rank Deep Learning Model MSE (*10 -2 , lower = better) MAE (*10 -1 , lower = better) #1 Neural Concept 7.80 1.44 #2 GAOT (May 2025) 8.74 1.57 #3 TripNet (March 2025) 9.52 2.15 #4 FIGConvNet (Feb. 2025) 9.86 2.22 #5 RegDGCNN (June 2024) 13.82 3.64 Table 2: Neural Concept’s Geometric Regressor predicts wall shear stress more accurately than previously published state-of-the-art methods. Figure 2: Side-by-side comparison of the ground truth magnitude of the wall shear stress, Neural Concept model’s prediction, and the corresponding error for a representative test sample. Across both surface fields (pressure and wall shear stress), the Geometric Regressor achieved the lowest MSE and MAE by a clear margin. The baseline methods represent several high-quality and recent academic work (the earliest being from June 2024), yet our architecture established a new state-of-the-art in predictive performance. 2. Volumetric Predictions: Velocity Beyond surface quantities, DrivAerNet++ provides 3D velocity fields in the flow volume surrounding the vehicle, which we also predicted using the Geometric Regressor. Rank Deep Learning Model MSE (lower = better) MAE (*10 -1 , lower = better) #1 Neural Concept 3.11 9.22 #2 TripNet (March 2025) 6.71 15.2 Table 3: Neural Concept’s Geometric Regressor predicts velocity more accurately than the previously published state-of-the-art method. The illustration below shows the velocity magnitude for two test samples. Note that only a single 2D slice of the 3D volumetric domain is shown here, focusing on the wake region behind the car. In practice, the network predicts velocity at any location within the full 3D domain, not just on this slice. Figure 3: Velocity magnitude for two test samples, arranged in two columns (left and right). For each sample, the top row displays the simulated velocity field, the middle row shows the prediction from the network, and the bottom row presents the error between the two. 3. Scalar Predictions: Drag Coefficient The drag coefficient (Cd) is the most critical parameter in automotive aerodynamics, as reducing it directly translates to lower fuel consumption in combustion vehicles and increased range in electric vehicles. Using the same underlying architecture, our model achieved state-of-the-art performance in Cd prediction. In addition to MSE and MAE, we reported the Maximum Absolute Error (Max AE) to reflect worst-case accuracy. We also included the Coefficient of Determination (R² score), which measures the proportion of variance explained by the model. An R² value of 1 indicates a perfect fit to the target data. Rank Deep Learning Model MSE (*1e-5) MAE (*1e-3) Max AE (*1e-2) R² #1 Neural Concept 0.8 2.22 1.13 0.978 #2 TripNet 9.1 7.19 7.70 0.957 #3 PointNet 14.9 9.60 12.45 0.643 #4 RegDGCNN 14.2 9.31 12.79 0.641 #5 GCNN 17.1 10.43 15.03 0.596 On the official split, the model shows tight agreement with CFD (R² of 0.978) across the test set, which is sufficient for early design screening where engineers need to rank variants confidently and spot meaningful gains without running full simulations for every change. 4. Compute Efficiency and Azure HPC&AI Collaboration Executing the full DrivAerNet++ benchmark at industrial scale required Neural Concept’s full software and infrastructure stack combined with seamless cloud integration on Microsoft Azure to dynamically scale computing resources on demand. The entire pipeline runs natively on Microsoft Azure and can scale within minutes, allowing us to process new industrial datasets that contain thousands of geometries without complex capacity planning. Dataset Scale and Ingestion DrivAerNet++ dataset contains 8000 car designs along with their corresponding CFD simulations. The raw dataset occupies approximately 39TB of storage. Generating the simulations required a total of about 3 million CPU hours by MIT’s DeCoDE Lab. Ingestion into Neural Concept’s platform is the first step of the pipeline. To convert the raw data into the platform’s native format, we use a Conversion task that transforms raw files into the platform’s optimized native format. This task was parallelized with 128 workers; each allocated 5 GB of RAM. As a result, the entire conversion process was completed in approximately one hour only. After converting the relevant data (car geometry, wall shear stress, pressure, and velocity), the full dataset occupies approximately 3 TB in Neural Concept’s native format. Data Pre-Processing Pre-processing the dataset required both large-scale parallelization and the application of our domain-specific best practices. During this phase, workloads were distributed across multiple compute nodes with peak memory usage reaching approximately 1.5 TB of RAM. The pre-processing pipeline consists of two main stages. In the first stage, we repaired the car meshes and pre-computed geometric features needed for training. The second stage involved filtering the volumetric domain and re-sampling points to follow a spatial distribution that is more efficient for training our deep learning model. We scaled the compute resources so that each of the two stages in the pipeline completes in 1 to 3 hours when processing the full dataset. The first stage is the most computationally intensive. To handle it efficiently, we parallelized the task across 256 independent workers, each allocated 6 GB of RAM. Model Training and Deployment While we use state-of-the-art hardware for training, our performance gains come primarily from model design. Once trained, the model remains lightweight and cost-effective to run. Training was performed on Azure Standard_NC96ads_A100_v4 node, which provided access to four A100 GPUs, each with 80 GB of memory. The model was trained for approximately 24 hours. Neural Concept’s Geometric Regressor achieved the best reported performance on the official benchmark for surface pressure, wall shear stress, volumetric velocity and drag prediction.
mpi-stage: High-Performance File Distribution for HPC Clusters
When running containerized workloads on HPC clusters, one of the first problems you hit is getting container images onto the nodes quickly and repeatably. A .sqsh is a Squashfs image (commonly used by container runtimes on HPC). In some environments you can run a Squashfs image directly from shared storage, but at scale that often turns the shared filesystem into a hot spot. Copying the image to local NVMe keeps startup time predictable and avoids hundreds of nodes hammering the same source during job launch. In this post, I'll introduce mpi-stage, a lightweight tool that uses MPI broadcasts to distribute large files across cluster nodes at speeds that can saturate the backend network. The Problem: Staging Files at Scale On an Azure CycleCloud Workspace for Slurm cluster with GB300 GPU nodes, I needed to stage a large Squashfs container image from shared storage onto each node's local NVMe storage before launching training jobs. At small scale you can often get away with ad-hoc copies, but once hundreds of nodes are all trying to read the same source file, the shared source filesystem quickly becomes the bottleneck. I tried several approaches: Attempt 1: Slurm's sbcast Slurm's built-in sbcast seemed like the natural choice. In my quick testing it was slower than I wanted, and the overwrite/skip-existing behavior didn't match the "fast no-op if already present" workflow I was after. I didn't spend much time exploring all the configuration options before moving on. Attempt 2: Shell Script Fan-Out I wrote a shell script using a tree-based fan-out approach: copy to N nodes, then each of those copies to N more, and so on. This worked and scaled reasonably, but had some drawbacks: Multiple stages: The script required orchestrating multiple rounds of copy commands, adding complexity Source filesystem stress: Even with fan-out, the initial copies still hit the source filesystem simultaneously — a fan-out of 4 meant 4 nodes competing for source bandwidth Frontend network: Copies went over the Ethernet network by default — I could have configured IPoIB, but that added more setup The Solution: MPI Broadcasts The key insight was that MPI's broadcast primitive (MPI_Bcast) is specifically optimized for one-to-many data distribution. Modern MPI implementations like HPC-X use tree-based algorithms that efficiently utilize the high-bandwidth, low-latency InfiniBand network. With mpi-stage: Single source read: Only one node reads from the source filesystem Backend network utilization: Data flows over InfiniBand using optimized MPI collectives Intelligent skipping: Nodes that already have the file (verified by size or checksum) skip the copy entirely Combined, this keeps the shared source (NFS, Lustre, blobfuse, etc.) from being hammered by many concurrent readers while still taking full advantage of the backend fabric. How It Works mpi-stage is designed around a simple workflow: The source node reads the file in chunks and streams each chunk via MPI_Bcast. Destination nodes write each chunk to local storage immediately upon receipt. This streaming approach means the entire file never needs to fit in memory — only a small buffer is required. Key Features Pre-copy Validation Before any data is transferred, each node checks if the destination file already exists and matches the source. You can choose between: Size check (default): Fast comparison of file sizes—sufficient for most use cases Checksum: Stronger validation, but requires reading the full file and is therefore slower If all nodes already have the correct file, mpi-stage completes in milliseconds with no data transfer. Double-Buffered Transfers The implementation uses double-buffered, chunked transfers to overlap network communication with disk I/O. While one buffer is being broadcast, the next chunk is being read from the source. Post-copy Validation Optionally verify that all nodes received the file correctly after the copy completes. Single-Writer Per Node The tool enforces one MPI rank per node to prevent filesystem contention and ensure predictable performance. Real-World Performance In one run using 156 GPU nodes, distributing a container image achieved approximately 3 GB/s effective distribution rate (file_size/time), completing in just over 5 seconds: [0] Copy required: yes [0] Starting copy phase (source writes: yes) [0] Copy complete, Bandwidth: 3007.14 MB/s [0] Post-validation complete [0] Timings (s): Topology check: 5.22463 Source metadata: 0.00803746 Pre-validation: 0.0046786 Copy phase: 5.21189 Post-validation: 2.2944e-05 Total time: 5.2563 Because every node writes the file to its own local NVMe, the cumulative write rate across the cluster is roughly this number times the node count: ~3 GB/s × 156 ≈ ~468 GB/s of total local writes. Workflow: Container Image Distribution The primary use case is distributing Squashfs images to local NVMe before launching containerized workloads. Run mpi-stage as a job step before your main application: #!/bin/bash #SBATCH --job-name=my-training-job #SBATCH --ntasks-per-node=1 #SBATCH --exclusive # Stage the container image srun --mpi=pmix ./mpi_stage \ --source /shared/images/pytorch.sqsh \ --dest /nvme/images/pytorch.sqsh \ --pre-validate size \ --verbose # Run the actual job (from local NVMe - much faster!) srun --container-image=/nvme/images/pytorch.sqsh ... mpi-stage will create the destination directory if it doesn't exist. If your container runtime supports running the image directly from shared storage, you may not strictly need this step—but staging to local NVMe tends to be faster and more predictable at large scale. Because of the pre-validation, you can include this step in every job script without penalty—if the image is already present, it completes in milliseconds. Getting Started git clone https://github.com/edwardsp/mpi-stage.git cd mpi-stage make For detailed usage and options, see the README. Summary mpi-stage started as a solution to a very specific problem—staging large container images efficiently across a large GPU cluster—but the same pattern may be useful in other scenarios where many nodes need the same large file. By using MPI broadcasts, only a single node reads from the source filesystem, while data is distributed over the backend network using optimized collectives. In practice, this can significantly reduce load on shared filesystems and cloud-backed mounts, such as Azure Blob Storage accessed via blobfuse2, where hundreds of concurrent readers can otherwise become a bottleneck. While container images were the initial focus, this approach could also be applied to staging training datasets, distributing model checkpoints or pretrained weights, or copying large binaries to local NVMe before a job starts. Anywhere that a “many nodes, same file” pattern exists is a potential fit. If you're running large-scale containerized workloads on Azure HPC infrastructure, give it a try. If you use mpi-stage in other workflows, I'd love to hear what worked (and what didn't). Feedback and contributions are welcome. Have questions or feedback? Leave a comment below or open an issue on GitHub.Monitoring HPC & AI Workloads on Azure H/N VMs Using Telegraf and Azure Monitor (GPU & InfiniBand)
As HPC & AI workloads continue to scale in complexity and performance demands, ensuring visibility into the underlying infrastructure becomes critical. This guide presents an essential monitoring solution for AI infrastructure deployed on Azure RDMA-enabled virtual machines (VMs), focusing on NVIDIA GPUs and Mellanox InfiniBand devices. By leveraging the Telegraf agent and Azure Monitor, this setup enables real-time collection and visualization of key hardware metrics, including GPU utilization, GPU memory usage, InfiniBand port errors, and link flaps. It provides operational insights vital for debugging, performance tuning, and capacity planning in high-performance AI environments. In this blog, we'll walk through the process of configuring Telegraf to collect and send GPU and InfiniBand monitoring metrics to Azure Monitor. This end-to-end guide covers all the essential steps to enable robust monitoring for NVIDIA GPUs and Mellanox InfiniBand devices, empowering you to track, analyze, and optimize performance across your HPC & AI infrastructure on Azure. DISCLAIMER: This is an unofficial configuration guide and is not supported by Microsoft. Please use it at your own discretion. The setup is provided "as-is" without any warranties, guarantees, or official support. While Azure Monitor offers robust monitoring capabilities for CPU, memory, storage, and networking, it does not natively support GPU or InfiniBand metrics for Azure H- or N-series VMs. To monitor GPU and InfiniBand performance, additional configuration using third-party tools—such as Telegraf—is required. As of the time of writing, Azure Monitor does not include built-in support for these metrics without external integrations. 🔔 Update: Supported Monitoring Option Now Available Update (December 2025): At the time this guide was written, monitoring InfiniBand (IB) and GPU metrics on Azure H-series and N-series VMs required a largely unofficial approach using Telegraf and Azure Monitor. Microsoft has since introduced a supported solution: Azure Managed Prometheus on VM / VM Scale Sets (VMSS), currently available in private preview. This new capability provides a native, managed Prometheus experience for collecting infrastructure and accelerator metrics directly from VMs and VMSS. It significantly simplifies deployment, lifecycle management, and long-term support compared to custom Telegraf-based setups. For new deployments, customers are encouraged to evaluate Azure Managed Prometheus on VM / VMSS as the preferred and supported approach for HPC and AI workload monitoring. Official announcement: Private Preview: Azure Managed Prometheus on VM / VMSS Step 1: Making changes in Azure for sending GPU and IB metrics from Telegraf agents to Azure monitor from VM or VMSS. Register the microsoft.insights resource provider in your Azure subscription. Refer: Resource providers and resource types - Azure Resource Manager | Microsoft Learn Step 2: Enable Managed Service Identities to authenticate an Azure VM or Azure VMSS. In the example we are using Managed Identity for authentication. You can also use User Managed Identities or Service Principle to authenticate the VM. Refer: telegraf/plugins/outputs/azure_monitor at release-1.15 · influxdata/telegraf (github.com) Step 3: Set Up the Telegraf Agent Inside the VM or VMSS to Send Data to Azure Monitor In this example, I'll use an Azure Standard_ND96asr_v4 VM with the Ubuntu-HPC 2204 image to configure the environment for VMSS. The Ubuntu-HPC 2204 image comes with pre-installed NVIDIA GPU drivers, CUDA, and InfiniBand drivers. If you opt for a different image, ensure that you manually install the necessary GPU drivers, CUDA toolkit, and InfiniBand driver. Next, download and run the gpu-ib-mon_setup.sh script to install the Telegraf agent on Ubuntu 22.04. This script will also configure the NVIDIA SMI input plugin and InfiniBand Input Plugin, along with setting up the Telegraf configuration to send data to Azure Monitor. Note: The gpu-ib-mon_setup.sh script is currently supported and tested only on Ubuntu 22.04. Please read the InfiniBand counter collected by Telegraf - https://enterprise-support.nvidia.com/s/article/understanding-mlx5-linux-counters-and-status-parameters Run the following commands: wget https://raw.githubusercontent.com/vinil-v/gpu-ib-monitoring/refs/heads/main/scripts/gpu-ib-mon_setup.sh -O gpu-ib-mon_setup.sh chmod +x gpu-ib-mon_setup.sh ./gpu-ib-mon_setup.sh Test the Telegraf configuration by executing the following command: sudo telegraf --config /etc/telegraf/telegraf.conf --test Step 4: Creating Dashboards in Azure Monitor to Check NVIDIA GPU and InfiniBand Usage Telegraf includes an output plugin specifically designed for Azure Monitor, allowing custom metrics to be sent directly to the platform. Since Azure Monitor supports a metric resolution of one minute, the Telegraf output plugin aggregates metrics into one-minute intervals and sends them to Azure Monitor at each flush cycle. Metrics from each Telegraf input plugin are stored in a separate Azure Monitor namespace, typically prefixed with Telegraf/ for easy identification. To visualize NVIDIA GPU usage, go to the Metrics section in the Azure portal: Set the scope to your VM. Choose the Metric Namespace as Telegraf/nvidia-smi. From there, you can select and display various GPU metrics such as utilization, memory usage, temperature, and more. In example we are using GPU memory_used metrics. Use filters and splits to analyze data across multiple GPUs or over time. To monitor InfiniBand performance, repeat the same process: In the Metrics section, set the scope to your VM. Select the Metric Namespace as Telegraf/infiniband. You can visualize metrics such as port status, data transmitted/received, and error counters. In this example, we are using a Link Flap Metrics to check the InfiniBand link flaps. Use filters to break down the data by port or metric type for deeper insights. Link_downed Metric Note: The link_downed metric with Aggregation: Count is returning incorrect values. We can use Max, Min values. Port_rcv_data metrics Creating custom dashboards in Azure Monitor with both Telegraf/nvidia-smi and Telegraf/infiniband namespaces allows for unified visibility into GPU and InfiniBand. Testing InfiniBand and GPU Usage If you're testing GPU metrics and need a reliable way to simulate multi-GPU workloads—especially over InfiniBand—here’s a straightforward solution using the NCCL benchmark suite. This method is ideal for verifying GPU and network monitoring setups. NCCL Benchmark and OpenMPI is part of the Ubuntu HPC 22.04 image. Update the variable according to your environment. Update the hostfile with the hostname. module load mpi/hpcx-v2.13.1 export CUDA_VISIBLE_DEVICES=2,3,0,1,6,7,4,5 mpirun -np 16 --map-by ppr:8:node -hostfile hostfile \ -mca coll_hcoll_enable 0 --bind-to numa \ -x NCCL_IB_PCI_RELAXED_ORDERING=1 \ -x LD_LIBRARY_PATH=/usr/local/nccl-rdma-sharp-plugins/lib:$LD_LIBRARY_PATH \ -x CUDA_DEVICE_ORDER=PCI_BUS_ID \ -x NCCL_SOCKET_IFNAME=eth0 \ -x NCCL_TOPO_FILE=/opt/microsoft/ndv4-topo.xml \ -x NCCL_DEBUG=WARN \ /opt/nccl-tests/build/all_reduce_perf -b 8 -e 8G -f 2 -g 1 -c 1 Alternate: GPU Load Simulation Using TensorFlow If you're looking for a more application-like load (e.g., distributed training), I’ve prepared a script that sets up a multi-GPU TensorFlow training environment using Anaconda. This is a great way to simulate real-world GPU workloads and validate your monitoring pipelines. To get started, run the following: wget -q https://raw.githubusercontent.com/vinil-v/gpu-monitoring/refs/heads/main/scripts/gpu_test_program.sh -O gpu_test_program.sh chmod +x gpu_test_program.sh ./gpu_test_program.sh With either method NCCL benchmarks or TensorFlow training you’ll be able to simulate realistic GPU usage and validate your GPU and InfiniBand monitoring setup with confidence. Happy testing! References: Ubuntu HPC on Azure ND A100 v4-series GPU VM Sizes Telegraf Azure Monitor Output Plugin (v1.15) Telegraf NVIDIA SMI Input Plugin (v1.15) Telegraf InfiniBand Input Plugin Documentation
