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Introduction

We are amid the AI “gold rush.” More organizations are looking to incorporate any form of machine learning (ML) or deep learning in their services to enhance customer experience, drive efficiencies in their processes or improve quality of life (healthcare, transportation, smart cities).  

Train Where Data is Generated

One of the key elements that drive on-premise ML focused infrastructure growth is the reality of data gravity. Mentioned in the article “Multi-GPU and Distributed Deep Learning,” deep learning (DL) gets better with data. Consequently, data sets used for ML and DL training purposes are growing at a tremendous rate. These vast data sets need to be processed. Data transit, hosting, and the necessary compute cycles impact the overall OPEX budget. Additionally, data protection regulations such as data residency, data sovereignty, and data locality impact where data can be stored outside the place it is created. As a result, a lot of forward-leaning organizations are repatriating their AI platforms to run ML and DL workloads close to the systems that generate the data. 

And what better platform to run ML and DL workloads than vSphere? Machine learning comes with its own set of lingo, and different personas interacting with the machine learning stack. To be able to have a meaningful conversation with data scientists and ML engineers, you need to have a basic understanding of how each component interacts with each other. You don’t have to learn the ins and out of the different neural networks, but having an idea of what a particular component does help you understand how it might impact your service levels and your selection of components of the vSphere platform used for ML workloads.

To give an example, OpenCL and Vulkan are frameworks that allow for the execution of code on GPU (General Purpose GPU). Using this framework allows you to theoretically expose any GPUs to a machine learning framework such as Tensorflow or Pytorch. As it’s open-source, you can use it on all kinds of GPUs from different vendors. However, all popular actively-developed frameworks do not support OpenCL or Vulkan and only use the NVIDIA CUDA API framework, thus impacting your hardware selection for the vSphere host design. I created an overview of the different layers of the deep learning technology stack, attempting to make sense of the relationships between the components of each different layer. 

Deep Learning Technology Stack

Let’s use a bottom-up approach for reviewing the deep learning technology stack.

vSphere Constructs and Accelerators

Hardware over the last 20 years looked uniform, other than the vendor and some minor vendor-specific functionality; the devices appeared relatively similar to a guest OS. Most code can run on an AMD as well as an Intel without changing. Hardware competed on scale and speed, not on different ways how it can interact with software. Today’s acceleration devices are very diverse and expose their explicit architecture to the application. The hardware specifics determine the code and algorithm used in the application. And therefore, we need to expose these devices to the application in its most raw and unique form. As a result, the overview primarily covers acceleration devices such as GPUs and FPGAs. 

However, recently Rice University released a new research paper called: “SLIDE: In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems“. They are demonstrating that by using a fundamentally different approach, it is possible to accelerate deep learning without using hardware accelerators like GPUs.

Our evaluations on industry-scale recommendation datasets, with large fully connected architectures, show that training with SLIDE on a 44 core CPU is more than 3.5 times (1-hour vs. 3.5 hours) faster than the same network trained using TF on Tesla V100 at any given accuracy level. On the same CPU hardware, SLIDE is over 10x faster than TF

I assume the authors meant a dual-socket system with two 22-cores Xeon CPUs, but an exciting development that certainly needs to be closely followed. For now, let’s concentrate on the components used by the majority of the deep-learning community.

vSphere can expose the accelerator devices via two constructs right now, DirectPath I/O (Passthrough) and NVIDIA vGPU. In 2020 two additional constructs will be available; Dynamic DirectPath I/O (information will follow soon) and Bitfusion. Bitfusion pools and shares accelerators across VMs inside the cluster. It provides virtual remote attached GPUs that can be shared between VMs fully or fractionally. Bitfusion can even assign multiple GPUs to a single VM to support any form of distributed deep learning strategy.

DirectPath I/O

DirectPath I/O, often called Passthrough, provides similar functionality as bare-metal GPU. DirectPath I/O is used for maximum hardware device performance inside the VM and to use native vendor driver and app stack support in the guest OS and the application. Perfect for running specialized software libraries provided by the CUDA stack, which is covered in a later paragraph. DirectPath I/O allows for maximum performance because the I/O mapped from the application and guest OS directly to the hardware device; the VMkernel (Hypervisor) is not involved. 

A complete device is assigned to a single VM and cannot be shared with other active VMs. This prohibits fractional use of the device (i.e., assigning half of the device resources to a VM). DirectPath I/O allows assigning multiple GPUs to a VM.

When a DirectPath I/O device is assigned to a virtual machine, it uses the physical location of the device for the assignment, i.e., Host:Bus: Device-Physical-Function. (The article “Machine Learning Workload and GPGPU NUMA Node Locality” describes locality assignment in detail). Due to this, DirectPath I/O is not compatible with certain core virtualization features, such as vMotion (and thus DRS).

Design Impact

Currently, vSphere does not support the AMD Radeon Server Accelerators for Deep learning (Radeon Instinct). vSphere supports only NVIDIA GPUs at the moment. From vSphere 6.7 update 1, FPGAs can also be directly exposed to the guest OS by using DirectPath I/O. At the time of writing this article, vSphere 6.7 update 1 supports the Intel Arria 10 GX FPGA. More details on the vSphere blog.

NVIDIA vGPU

NVIDIA virtual GPU (vGPU) provides advanced GPU virtualization functionality that enables the sharing of GPU devices across VMs. An NVIDIA GPU can be logically partitioned (fractional GPU) to multiple virtual GPUs. A VM can use multiple vGPUs that are located in the same host. Both the hypervisor and the VM need to run NVIDIA software to provide fractional, full, and multiple vGPU functionalities.

Bitfusion

In 2019, VMware acquired Bitfusion, and I’m looking forward to having this functionality available to our customers. Bitfusion FlexDirect software allows for pooling GPU resources and providing a dynamic remote attach service. That means that workload can run on vSphere hosts that do not have GPU hardware installed. The beauty of this solution is that it does not require any changes to the application. It uses native CUDA (see acceleration libraries paragraph) to intercept the application calls, the FlexDirect software sends it to the FlexDirect server across the network. The Flexdirect server has a DirectPath I/O connection to all the GPUs in that host and manages the placement and scheduling of the workloads.

This model corresponds heavily to the early days of virtualization. We used to have 1000’s x86 servers in the data center, each having an average utilization of less than 10% while costing a lot of money. We consolidated compute resources and managed the workload in such a manner than peak utilization did not overlap. With the rise of general-purpose computing on GPU, we see the same patterns. The GPUs are not cheap, sometimes the cost of a GPU server is an order of magnitude more expensive than a “traditional” server. However, when we look at the utilization, we see an average usage of 5 to 20%.

Deep Learning Development Cycle
With deep-learning, you cannot just pump some data into a deep-learning model and expect a result. The data scientist has to gather training data, asses the data quality. The next step is to choose an algorithm and a framework. Often the data is not formatted correctly for the used model. The data set needs to be improved; typically, data scientists need to deal with outliers and extreme values, missing or inaccurate data. Data for supervised learning needs to be labeled, and the data set needs to be split up into training data sets and evaluation data sets. Now the deep-learning can begin, and the GPU is fed the data. The deep learning framework executes the model. After a single epoch, the data scientist reviews the effectiveness of the model and possibly adjust the model to improve prediction. The model is trained again to verify if the adjustments are correct. Once the model behaves appropriately, it deployed to production where it can run inference tasks that generate predictions based on new data. Each step consuming a lot of time, however only two moments (marked in red) utilize the expensive GPU hardware. Creating the problem, interestingly called “dark silicon”.

It doesn’t make sense to keep those resource isolated and assigned to a VM that can only be used by a specific data scientist. By introducing remote virtualization, a GPU can be shared between many different virtual machines. A Bitfusion server can contain multiple GPUs, and many Bitfusion servers can be active on the network. Abstracting the hardware and allow for remote execution of API calls, creates a solution that is easily scaled out. Orchestrating workload placement ensures that a pool of GPUs can be made available to the data scientist when the model is ready for training.

During the Tech Field Day, Mazhar Memom (CTO Bitfusion) covered the Bitfusion architecture, showing the use of CUDA libraries by the application to interact with the GPU device. In Bitfusion’s case, it is sending these remote API calls to a server that controls the hardware. But this brings us to the statement made earlier in the article. We have arrived at a time in which the software depends heavily on abstraction layers. In the AI space, it is no different. A deep learning model is going to use a framework that uses a set of libraries that are provided by the hardware vendor. This model allows the application developer to quickly (and correctly) to consume the hardware functionality to drive application performance. The defacto toolkit for the deep learning ecosystem is NVIDIA’s Compute Unified Device Architecture (CUDA). The next article covers the subsequent layers in the DL framework stack in more depth.

More enterprises are incorporating machine learning (ML) into their operations, products, and services. Similar to other workloads, a hybrid-cloud model strategy is used for ML development and deployment. A common strategy is using the excellent toolset and training data offered by public cloud ML services for generic ML capabilities. These ML activities typically improve an organization’s quality of service and increase in productivity. But the real differentiation lies within using the organization’s unique data and know-how to create what’s called differentiated machine learning. The data used is primarily generated by own processes or through interaction with its customers. As a result, specific rules and regulations come into play when handling and storing that data. Another strong aspect of determining where to deploy ML activities is data gravity. Placing compute close to where the data is generated provides a consistent (often high-performing) service. As a result, many organizations invest in the infrastructure needed to deploy ML and deep learning (DL) solutions.

Deep Learning

Deep learning is a subset of the more extensive collection of machine learning techniques. The critical difference between ML and DL is the way the data is presented to the solution. ML uses mathematical techniques and data to build predictive models. It uses labeled (structured) data to train the model, and once the model is trained accurately enough, the model keeps on learning by feeding new data. Deep learning does not necessarily need structured or labeled data to create an accurate model to provide a predictive answer. It uses larger neural networks (layers of algorithms, imitating the brain’s neural network), and it needs to be fed vast amounts of data to provide an accurate prediction.  

Interestingly, at one point, ML experiences a performance plateau regardless of the amount of incoming new data, while deep learning keeps on improving. For more information, about this phenomenon review the notes from Andrew Ng Coursera Deep Learning course or watch his 5-minute clip on youtube: How Scale is Enabling Deep Learning.

In essence, the magic of deep-learning is that it gets better with data, and thus, how do we create an infrastructure that is capable of feeding, transporting, and processing these vast amounts of data, while still being able to run non-ML/DL workload?

Parallelism

The best way of dealing with massive amounts of data is to process it in a parallel way. And that’s where general-purpose computing on GPU (GPGPU) comes into play. A simple TensorFlow test compared the performance between a dual AMD Opteron 6168 (2×12 cores) vs. a system with a (consumer-grade NVIDIA Geforce 1070. The AMD system recorded 440 examples per second, while the Geforce processed 6500 examples per second. There are many performance tests available, but this showed the power of a consumer-grade GPU versus a data center grade CPU system.

Today data center focused GPUs have more than 5000 cores all optimized to operate in parallel. These cores have access to 32 GB of high bandwidth memory (HBM2) with speeds up to 900 GB/s (theoretical bandwidth). According to the paper “Analysis of Relationship between SIMD-Processing Features Used in NVIDIA GPUs and NEC SX-Aurora TSUBASA Vector Processors” by Ilya.V. Afanasyev et al. the achievable bandwidth on the tested NVIDIA Volta V100 was 809 GB/s. Getting all the data loaded in memory with consistent performance is one element that impacts virtual machine design. See “Machine Learning Workload and GPGPU NUMA Node Locality” for more information.

Although the improvement of processing speed is enormous, up to 10x over a CPU according to this performance study, sometimes this speed-up is not enough. After processing all the training examples in a dataset (called an epoch), a data scientist might make some adjustments as well and start another epoch to improve the prediction model.

It’s common to run multiple epochs before getting an adequate trained model (and in the process pushing lots of data through the system). Reducing training time, allows the organization to deploy the trained model faster, and start benefiting from their ML and DL initiatives. A “simple” way to reduce training time is to use multiple GPU devices to increase parallelism.

Distributed Deep Learning Strategies

How do you scale out your training model across the multiple GPUs in your system? You add another layer of parallelism on top of GPUs. Parallelism is a common strategy is distributed deep learning. There are two popular methods of parallelizing DL models: model parallelism and data parallelism.

Model parallelism
With model parallelism, a single model (Neural Network A) is split and distributed across different GPUs (GPU0 and GPU1). The same (full) training data will be processed by the different GPUs depending on which layer is active. Models with a very large number of parameters, that are too big to fit inside a single device’s memory, benefit from this type of strategy.

Neural networks have data dependency. The output of the previous layer is the input of the next layer. Asynchronous processing of data can be used to reduce training time, however, model parallelism is more about having the ability to run large models.

Maybe model sequentiality would be a better name for this mode as it primarily is using devices in sequential order. More than often a device is idling, waiting to receive the data from another device. Once the model part is trained on one device, it has to synchronize the outcome with the next layer possibly handled by another device.

This synchronization is interesting when designing your ML platform as specific data to help run the model has to traverse the interconnect either between devices within the ESXi system or between VMs (or containers) running on the platform. More about this in a later paragraph.

Data parallelism
Data parallelism is the most common strategy deployed. As covered in the previous article: “Machine Learning Workload and GPGPU NUMA node locality” it is common to split up the entire training dataset into batches (batch 0 and batch1). With data parallelism, these batches are sent to the multiple GPUs (GPU 0 and GPU1). Each GPU will load a full replica of the model (Neural Network A) and run their batch of training examples through the model.

The models running on the GPUs must communicate with each other to share the results. Communication timing and patterns between the GPUs depend on the DL model ( Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN)) and on the framework used (TensorFlow, Pytorch, MXNet).

Currently, there are a few projects active that are exploring the possibility of hybrid parallelization. This strategy uses both model and data parallelization strategies to minimize end-to-end training time.

Parallelism introduces communication between GPUs. Understanding the data-flow is essential to build a system that can provide consistent high-performance while ensuring the DL workloads are isolated enough and do not impact other workloads that are using the system. Various distributions of GPU resources are possible, such as a cluster of single GPU systems or multi-GPUs hosts. The next article focusses only on a single node with a multi-GPU configuration, to highlight the different in-system (on-node) interconnects

On-Node Interconnect

vSphere allows for different multi-GPU configurations. A VM can be equipped with multiple GPU configured as a passthrough device, or configured with vGPUs with the help of NVIDIA drivers, or by using a Bitfusion solution. Details about the different solutions will be covered in a future article. But regardless of the chosen configuration, the application will be able to use multiple GPUs in a single VM.

When deploying deep learning models across multiple GPUs in a single VM, the ESXi host PCIe bus becomes an inter-GPU network that is used for loading the data from system memory into the device memory. Once the model is active, the PCIe bus is used for GPU to GPU communication for synchronization between models or communication between layers.

If two PCIe devices communicate with each other, then the CPU is involved. Data coming from the source device is stored in system memory before transferring it to the destination device. The new Skylake architecture with it’s updated IIO structure, and additional mesh stops improved the CPU to PCIe communication over the previous ring-based architecture featured on the Xeon v1 through v4. (Each mesh stop has a dedicated cache and traffic controller).

For this purpose, NVIDIA introduced GPUDirect in CUDA 4.0, allowing direct memory access between two devices. However, this requires a full topology view of the system, and this is something currently vSphere is not exposing. As such, no direct PCI to PCI communication is available (yet).

Discovering this seems like this lack of topology view is an enormous bottleneck, but this doesn’t necessarily mean an application performance slowdown. Modern frameworks optimize their GPU code to minimize communication. As a result, communication between devices is just a portion of total time. Depending on the framework used and the parallelism strategy, the performance can still be close to the bare-metal performance.

NVIDIA NVLink

In 2016, NVIDIA introduced the NVLink interconnect, a high-speed mesh network that allows GPUs to communicate directly with each other. NVLink is designed to replace the inter GPU-GPU communication across the PCIe lanes, and as a result, NVLINK uses a separate interconnect. A new custom form factor SXM2 (supported by vSphere) allows the GPU to interface with the NVIDIA High-Speed Signalling interconnect (NVHS). The NVHS allows the GPU to communicate with the other GPUs as well as direct system memory access. Currently, NVLink 2.0 (available on NVIDIA Tesla v100 GPUs) provides an aggregate maximum theoretical bidirectional bandwidth of 300 GBps. (AMD does not have any equivalent to NVlink)

Design Decisions

Data movement within an ML system (VM) can be substantial. Fetching the data from storage, storing it into system memory before dispatching it to multiple vGPUs can produce a significant load on the platform. Depending on the neural network, framework, and parallelism strategy, communication between GPUs can add additional load to the system. It’s key to understand this behavior before considering retrofitting your current platform with GPU devices or while designing your new vSphere clusters.

Depending on the purpose of the platform it might be interesting to research the value of having a separate interconnect mesh for ML/DL workload. It allows for incredible isolation that will enable you to run other workloads on the ESXi host as well. Couple this the ability to share multiple GPUs with the Bitfusion solution, and you can create a platform that provides consistent high-performance for ML workload to numerous data scientists.