High-performance computing (HPC) accelerates breakthroughs in scientific research, engineering, drug discovery and climate modeling. But optimizing HPC to deliver those results is a complex science in itself as it is an intricate interplay of hardware and software.
So, it is critical for organizations running these systems to understand these intricacies and nuances so that they can optimize performance, control costs, and accelerate innovation.
HPC systems are comprised of specialized components that work together to deliver unparalleled computational power. Let's break down the core elements:
Compute Hardware
At the heart of any HPC system lie the CPUs, GPUs, and memory. This isn't your average desktop setup; we're talking about high-performance components designed to crunch numbers at astonishing speeds.
Data Storage
Computations are solved in steps and when each step is solved, the solutions need to be written to disk before starting the next step. And high fidelity result files are large, we’re talking hundreds of Gigabytes in some cases. High speed data storage is necessary to prevent data writing from becoming a bottleneck. For this reason, specialized Parallel File Systems like Lustre and RAID systems are used.
Networking
The high performance in HPC comes due to distributing and solving computations over hundreds, thousands or even millions of cores, to combine their power. That can only be accomplished if data can be shared at lightning speeds. High-bandwidth low-latency networks like InfiniBand and Ultra Ethernet ensure rapid data transfer between cores and processors.
Supporting Infrastructure
Let's not forget about supporting infrastructure. HPC systems generate significant heat, demanding robust cooling solutions to maintain optimal operating temperatures. Efficient power management is also crucial to minimize energy consumption and costs.
Hardware is just the beginning. HPC systems rely on specialized software to manage resources, orchestrate tasks, and enable complex computations. This includes:
These software layers also include job schedulers and resource managers - such as Slurm Workload Manager, PBS Pro, or IBM LSF - responsible for efficiently distributing computing tasks across a cluster. By automating job queuing and resource allocation, these tools ensure that processors, memory, and storage are used to their fullest potential.
Beyond scheduling, they track job statuses and even handle system failures by restarting tasks or reallocating resources to avoid downtime. In tandem, environment modules or package managers help maintain consistent software setups across the entire cluster, ensuring each application has the exact libraries, compilers, and runtime environments it needs to run consistently.
Additionally, HPC systems often employ specialized monitoring and profiling tools that collect performance data in real-time. This data is instrumental in diagnosing bottlenecks and fine-tuning configurations for maximum efficiency. Combined with advanced debugging and visualization utilities, HPC software transforms raw hardware into a well-orchestrated powerhouse capable of handling some of the most computationally intensive workloads imaginable.
By integrating job schedulers, environment modules, and monitoring solutions, organizations can strike the perfect balance between performance, reliability, and manageability, breathing life into the powerful machines that fuel scientific and engineering breakthroughs.
We would be missing the point if we didn’t talk about the applications the researchers and engineers use to perform their research. Over the years, millions of computational software applications have been developed to solve highly specific scientific and engineering problems. Many evolved into open-source and commercially licensed tools that are used widely in industries. And each one of them has developed their own distinct way of running and scaling on HPC systems.
These applications also rely on a set of internal libraries and external libraries to operate and have compatibility with specific versions of operating systems, MPI libraries, Fortran runtimes, and visualization toolkits. And these compatibilities have converged and diverged with other computational software in this ecosystem over the history of their development.
The inherent complexities introduced by the nuances of HPC systems pose significant challenges to managing them effectively. These challenges span the entire lifecycle – from initial packaging and installation to the intricacies of hardware configuration and optimization of communication. Operational teams must possess deep expertise not only in HPC infrastructure but also in the specific applications and computational problems being addressed to ensure efficient execution.
Also, the scale of these environments, often encompassing tens or even hundreds of applications with multiple versions at each site, magnifies the complexity. Building and maintaining a shared software environment capable of supporting such a diverse portfolio of applications represents a significant engineering undertaking.
However, solutions like Simr are changing this reality by democratizing access to High-Performance Computing (HPC) through simplifying the process of running complex engineering simulations in the cloud. This approach removes the need for complex on-premise infrastructure and specialized IT knowledge, allowing users to focus on their core engineering objectives rather than the underlying technical complexities.
Well, let's say that someone has managed to accomplish all this and move on. What if a new software version introduces an additional dependency? Things start to break and trying to fix something in such a complex environment is like finding a needle in a haystack. What if we took the same technology that solved this similar problem for microservices and webservices and applied it here? What if the individual applications and the related workflows were made portable?
Fully harnessing the true potential of HPC is notoriously challenging due to the intricate interdependencies between hardware, software, and data as discussed in the previous section. These elements are often tightly coupled, creating a fragile ecosystem where even minor changes in one area can disrupt the entire workflow.
Imagine trying to move a complex simulation that relies on specific GPUs and a particular version of a software library to a new cluster with different hardware or a newer software version. The result? Compatibility issues, unexpected errors, and significant time spent troubleshooting and reconfiguring the workflow.
Understanding the dependencies between hardware, software, and data in HPC is only the first step. The real challenge lies in overcoming these dependencies to achieve true workflow portability: the ability to move workloads seamlessly across different environments without breaking functionality or losing efficiency. As you keep reading, we'll explore why portability is not just a convenience but a necessity for modern HPC operations.
"Workflow portability" refers to the ability to seamlessly move complex computational tasks between different computing environments. It involves encapsulating computational software, its dependencies, HPC libraries and the data associated with the workflows in a Docker container so that it doesn’t rely on the host operating system for them. Once containerized and portable, the workflow can run on any machine running any operating system as long as it is running the container runtime. This removes the risk associated with failing hardware and also the risk of shared dependencies.
But why is this ability to move workflows around so crucial? Consider a scenario in automotive design. Engineers are running complex crash simulations on their on-premises HPC cluster.
Suddenly, they need to significantly increase the scale of their simulations to analyze a new design with greater fidelity. Their local cluster lacks the capacity to handle this increased demand.
Scenarios needing transferring simulation workflows to a new hardware or environment:
Current hardware is failed
Datacenter upgrade or change
Current hardware is underpowered or overpowered for current workflow
Need for more compute capacity
Lack of compatibility in hardware to simulation software
Adoption of cloud initiative or reversal
With workflow portability, they could seamlessly transfer these simulations to a cloud-based HPC environment, leveraging the cloud's scalability and flexibility to meet their needs. Without it, they might face delays, limited analysis capabilities, or the costly and time-consuming process of procuring and configuring new hardware.
Use of Containers to achieve portability and eliminate dependency issues is standard operating procedure for most web-based application deployments. Despite all the benefits of containers, there are high barriers of entry for HPC workloads to harness containers. Handling MPI related complexities for HPC workloads that utilize multiple nodes to solve parallel jobs has been a big roadblock. Since most of the HPC systems are on-premises, the virtualization of these systems to support containers and Kubernetes orchestrators needs rearchitecting of the whole hardware stack. Due to these uphill challenges, the benefits of containerization and true portability have failed to penetrate the HPC realm. This has led to the continuation of age-old HPC practices which have led to the high transition costs.
This lack of portability hinders agility. Organizations may be locked into specific hardware or software vendors, unable to easily adapt to changing needs or take advantage of new technologies. It also impacts efficiency, as valuable time and resources are wasted on resolving compatibility issues and manually adapting workflows to new environments.
Furthermore, limited portability can lead to underutilization of resources. If a workflow is tied to a specific cluster that is not always fully utilized, valuable computing power may sit idle while other teams or projects may be waiting for access.
The challenges of HPC portability create a significant barrier for organizations seeking to maximize the value of their HPC investments. It hinders innovation, slows down research, and limits the ability to respond quickly to new opportunities or challenges.
There is a high cost to these consequences. HPC systems are mission critical systems in many manufacturing organizations and when engineers are unable to use them, the loss of productivity can cost the organization hundreds of thousands to even millions of dollars
At Simr, we understand the complexities and frustrations associated with HPC portability. Our expertise lies in developing solutions that simplify HPC operations and empower organizations to unlock the full potential of their infrastructure. Our approach centers around HPC-specific containers, a technology that addresses the challenges of portability head-on. In essence, we’re bringing the proven state of the art portability practices to HPC and CAE.
Think of a container as a lightweight, portable package that encapsulates everything a workflow needs to run–the application, its dependencies, libraries, and even the underlying operating system. This self-contained environment ensures consistency and eliminates the compatibility issues that often plague HPC deployments.
By containerizing HPC workflows, Simr enables seamless portability across different environments. Need to migrate a simulation to the cloud? No problem. Want to move a workflow to a new cluster with different hardware? Easy. Containers abstract away the underlying infrastructure, making these transitions smooth and efficient.
But the benefits go beyond portability. Containers also simplify management. Instead of painstakingly installing and configuring software on each new system, IT teams can deploy pre-built containers with everything pre-configured. This reduces complexity, minimizes errors, and frees up valuable time for more strategic tasks.
Furthermore, containerization enhances efficiency. By decoupling workflows from specific hardware, organizations can optimize resource utilization. Containers can be easily moved to where resources are available, maximizing the use of existing infrastructure and minimizing idle time. This agility also reduces downtime, as failed workflows can be quickly redeployed to another environment with minimal disruption.
Simr's HPC-specific containers provide a robust and adaptable solution for organizations seeking to overcome the challenges of HPC portability. We empower IT and engineering teams to streamline operations, accelerate innovation, and maximize the value of their HPC investments.
Navigating the complexities of HPC architecture and overcoming the challenges of portability are crucial steps for organizations seeking to maximize the value of their HPC investments. A deep understanding of the interplay between hardware, software, and data empowers organizations to optimize performance, streamline operations, and accelerate innovation.
At Simr, we are dedicated to simplifying HPC for our clients. Our HPC-specific container technology enables seamless workflow portability, reduces management complexity, and enhances resource utilization. We empower organizations to break free from the limitations of traditional HPC deployments and embrace a more agile and efficient approach to high-performance computing.
Ready to explore how Simr can help your organization unlock the full potential of HPC? Contact us today to learn more about our solutions and discover how we can help you achieve your computational goals.