Deploy NDm_v4 (A100) Kubernetes Cluster
We show how to deploy an optimal NDm_v4 (A100) AKS cluster, making sure that all 8 GPU and 8 InfiniBand devices available on each vritual machine come up correctly and are available to deliver optimal performance. A multi-node NCCL allreduce job is executed on the NDmv4 AKS cluster to verify its deployed/configured correctly.Announcing Azure HBv5 Virtual Machines: A Breakthrough in Memory Bandwidth for HPC
Discover the new Azure HBv5 Virtual Machines, unveiled at Microsoft Ignite, designed for high-performance computing applications. With up to 7 TB/s of memory bandwidth and custom 4th Generation EPYC processors, these VMs are optimized for the most memory-intensive HPC workloads. Sign up for the preview starting in the first half of 2025 and see them in action at Supercomputing 2024 in Atlanta
Performance & Scalability of HBv4 and HX-Series VMs with Genoa-X CPUs
Azure has announced the general availability of Azure HBv4-series and HX-series virtual machines (VMs) for high performance computing (HPC). This blog provides in-depth technical and performance information about these HPC-optimized VMs.Training large AI models on Azure using CycleCloud + Slurm
Here we demonstrate and provide template to deploy a computing environment optimized to train a transformer-based large language model on Azure using CycleCloud, a tool to orchestrate and manage HPC environments, to provision a cluster comprised of A100, or H100, nodes managed by Slurm. Such environments have been deployed to train foundational models with 10-100s billions of parameters on terabytes of data.
Benchmarking 6th gen. Intel-based Dv6 (preview) VM SKUs for HPC Workloads in Financial Services
Introduction In the fast-paced world of Financial Services, High-Performance Computing (HPC) systems in the cloud have become indispensable. From instrument pricing and risk evaluations to portfolio optimizations and regulatory workloads like CVA and FRTB, the flexibility and scalability of cloud deployments are transforming the industry. Unlike traditional HPC systems that require complex parallelization frameworks (e.g. depending on MPI and InfiniBand networking), many financial calculations can be efficiently executed on general-purpose SKUs in Azure. Depending on the codes used to perform the calculations, many implementations leverage vendor-specific optimizations such as AVX-512 from Intel. With the recent announcement of the public preview of the 6th generation of Intel-based Dv6 VMs (see here), this article will explore the performance evolution across three generations of D32ds – from D32dsv4 to D32dsv6. We will follow the testing methodology similar to the article from January 2023 – “Benchmarking on Azure HPC SKUs for Financial Services Workloads” (link here). Overview of D-Series VM in focus: In the official announcement it was mentioned, that the upcoming Dv6 series (currently in preview) offers significant improvements over the previous Dv5 generation. Key highlights include: Up to 27% higher vCPU performance and a threefold increase in L3 cache compared to the previous generation Intel Dl/D/Ev5 VMs. Support for up to 192 vCPUs and more than 18 GiB of memory. Azure Boost, which provides: Up to 400,000 IOPS and 12 GB/s remote storage throughput. Up to 200 Gbps VM network bandwidth. A 46% increase in local SSD capacity and more than three times the read IOPS. NVMe interface for both local and remote disks. Note: Enhanced security through Total Memory Encryption (TME) technology is not activated in the preview deployment and will be benchmarked once available. Technical Specifications for 3 generations of D32ds SKUs VM Name D32ds_v4 D32ds_v5 D32ds_v6 Number of vCPUs 32 32 32 InfiniBand N/A N/A N/A Processor Intel® Xeon® Platinum 8370C (Ice Lake) or Intel® Xeon® Platinum 8272CL (Cascade Lake) Intel® Xeon® Platinum 8370C (Ice Lake) Intel® Xeon® Platinum 8573C (Emerald Rapids) processor Peak CPU Frequency 3.4 GHz 3.5 GHz 3.0 GHz RAM per VM 128 GB 128 GB 128 GB RAM per core 4 GB 4 GB 4 GB Attached Disk 1200 SSD 1200 SSD 440 SSD Benchmarking Setup For our benchmarking setup, we utilised the user-friendly, open-source test suite from Phoronix (link) to run 2 tests from OpenBenchmarking.org test suite, specifically targeting quantitative finance workloads. The tests in the "finance suite" are divided into two groups, each running independent benchmarks. In addition to the finance test suite, we also ran the AI-Benchmark to evaluate the evolution of AI inferencing capabilities across three VM generations. Finance Bench QuantLib AI Benchmark Bonds OpenMP Size XXS Device Inference Score Repo OpenMP Size X Device AI Score Monte-Carlo OpenMP Device Training Score Software dependencies Component Version OS Image Ubuntu marketplace image: 24_04-lts Phoronix Test Suite 10.8.5 Quantlib Benchmark 1.35-dev Finance Bench Benchmark 2016-07-25 AI Benchmark Alpha 0.1.2 Python 3.12.3 To run the benchmark on a freshly created D-Series VM, execute the following commands (after updating the installed packages to the latest version): git clone https://github.com/phoronix-test-suite/phoronix-test-suite.git sudo apt-get install php-cli php-xml cmake sudo ./install-sh phoronix-test-suite benchmark finance For the AI Benchmark tests, a few additional steps are required. For example, creating a virtual environment for additional python packages and the installation of the tensorflow and ai-benchmark packages are required: sudo apt install python3 python3-pip python3-virtualenv mkdir ai-benchmark && cd ai-benchmark virtualenv virtualenv source virtualenv/bin/activate pip install tensorflow pip install ai-benchmark phoronix-test-suite benchmark ai-benchmark Benchmarking Runtimes and Results The purpose of this article is to share the results of a set of benchmarks that closely align with the use cases mentioned in the introduction. Most of these use cases are predominantly CPU-bound, which is why we have limited the benchmark to D-Series VMs. For memory-bound codes that would benefit from a higher memory-to-core ratio, the new Ev6 SKU could be a suitable option. In the picture below, you can see a representative benchmarking run on a Dv6 VM, where nearly 100% of the CPUs were utilised during execution. The individual runs of the Phoronix test suite, starting with Finance Bench and followed by QuantLib, are clearly visible. Runtimes Benchmark VM Size Start Time End Time Duration Minutes Finance Benchmark Standard D32ds v4 12:08 15:29 03:21 201.00 Finance Benchmark Standard D32ds v5 11:38 14:12 02:34 154.00 Finance Benchmark Standard D32ds v6 11:39 13:27 01:48 108.00 Finance Bench Results QuantLib Results AI Benchmark Alpha Results Discussion of the results The results show significant performance improvements in QuantLib across the D32v4, D32v5, and D32v6 versions. Specifically, the tasks per second for Size S increased by 47.18% from D32v5 to D32v6, while Size XXS saw an increase of 45.55%. Benchmark times for 'Repo OpenMP' and 'Bonds OpenMP' also decreased, indicating better performance. 'Repo OpenMP' times were reduced by 18.72% from D32v4 to D32v5 and by 20.46% from D32v5 to D32v6. Similarly, 'Bonds OpenMP' times decreased by 11.98% from D32v4 to D32v5 and by 18.61% from D32v5 to D32v6. In terms of Monte-Carlo OpenMP performance, the D32v6 showed the best results with a time of 51,927.04 ms, followed by the D32v5 at 56,443.91 ms, and the D32v4 at 57,093.94 ms. The improvements were -1.14% from D32v4 to D32v5 and -8.00% from D32v5 to D32v6. AI Benchmark Alpha scores for device inference and training also improved significantly. Inference scores increased by 15.22% from D32v4 to D32v5 and by 42.41% from D32v5 to D32v6. Training scores saw an increase of 21.82% from D32v4 to D32v5 and 43.49% from D32v5 to D32v6. Finally, Device AI scores improved across the versions, with D32v4 scoring 6726, D32v5 scoring 7996, and D32v6 scoring 11436. The percentage increases were 18.88% from D32v4 to D32v5 and 43.02% from D32v5 to D32v6. Next Steps & Final Comments The public preview of the new Intel SKUs have already shown very promising benchmarking results, indicating a significant performance improvement compared to the previous D-series generations, which are still widely used in FSI scenarios. It's important to note that your custom code or purchased libraries might exhibit different characteristics than the benchmarks selected. Therefore, we recommend validating the performance indicators with your own setup. In this benchmarking setup, we have not disabled Hyper-Threading on the CPUs, so the available cores are exposed as virtual cores. If this scenario is of interest to you, please reach out to the authors for more information. Additionally, Azure offers a wide range of VM families to suit various needs, including F, FX, Fa, D, Da, E, Ea, and specialized HPC SKUs like HC and HB VMs. A dedicated validation, based on your individual code / workload, is recommended here as well, to ensure the best suited SKU is selected for the task at hand.
Optimizing Language Model Inference on Azure
Inefficient inference optimization can lead to skyrocketing costs for customers, making it crucial to establish clear performance benchmarking numbers. This blog sets the standard for expected performance, helping customers make informed decisions that maximize efficiency and minimize expenses with the new Azure ND H200 v5-series.Exploring CPU vs GPU Speed in AI Training: A Demonstration with TensorFlow
In the ever-evolving landscape of artificial intelligence, the speed of model training is a crucial factor that can significantly impact the development and deployment of AI applications. Central Processing Units (CPUs) and Graphics Processing Units (GPUs) are two types of processors commonly used for this purpose. In this blog post, we will delve into a practical demonstration using TensorFlow to showcase the speed differences between CPU and GPU when training a deep learning model.Accelerating AI applications using the JAX framework on Azure’s NDm A100 v4 Virtual Machines
The results highlight good scaling from 1 to 16 nodes on both the Large and XLarge T5 models running with JAX on Azure. The Large T5 model has a scaling efficiency of 84% at 16 nodes (128 GPUs) while the XL T5 model has a scaling efficiency of 82% at 16 nodes (128 GPUs). The throughput is within 5% as compared to the NVIDIA DGX A100 data reported here. Customers can now use the JAX framework on Azure when training Large Language Models (LLMs) with solid scaling performance
