Today, high-performance processing is in use by multiple domains when dealing with sophisticated computational problems. With the help of high-performance computing systems, multiple situations like climate change and drug development are generated through molecular modeling.

This is very critical to the organizations involved in very demanding computational workloads because the right [**HPC**](https://www.lenovo.com/de/de/servers-storage/solutions/hpc/) infrastructure for them is an essential requirement. These workloads require incredible computing power and will frequently process huge amounts of information in parallel on a huge pool of CLUs. It’s not just 10’s or 100’s of CPUs; it’s thousands of cores, GPU accelerators, and other specialized hardware working in a single live orchestration.

In the following post, we will cover ten HPC system guidelines that can be used to handle applications with heavy workload volumes.

### 1. Powerful CPUs

The central processing units (CPUs) are the brains of any computing system and are critical for handling compute-intensive tasks. For HPC computing workloads, opting for the latest generation of high-core-count CPUs from Intel or AMD is recommended. Look for CPUs with at least 24 cores and support for technologies like AVX-512, which can accelerate floating-point operations. Many demanding workloads also benefit from having access to a large number of CPU cores to distribute work across in a parallel manner. Systems with over 100 CPU cores are common for high-performance processing use cases.

### 2. High-bandwidth Memory

The memory subsystem is another important component that can impact performance, especially for cloud HPC workloads involving large in-memory datasets. Opt for memory technologies like DDR4 that offer high bandwidth to keep pace with multi-core CPUs. Look for systems with at least 128GB of memory per node and support for non-uniform memory access (NUMA) architectures. This allows applications to access local memory banks faster than remote memory. Some systems also utilize high-bandwidth memory (HBM), which provides even greater throughput to feed massive computational workloads.

### 3. High-performance Networking

Networking is critical to enable communication between CPU cores and nodes in a clustered environment. HPC systems require ultra-low-latency, high-bandwidth networks to efficiently distribute work and aggregate results. Technologies like InfiniBand and OmniPath are well-suited for this purpose and can provide over 100 gigabits per second of bandwidth with microsecond-level latencies. The network switch architecture also plays a key role. A fat-tree or dragonfly+ design is ideal for large clusters to minimize congestion.

### 4. Parallel File Systems

Most high-performance computing workloads need to load large datasets from storage and write outputs back. This places huge demands on the underlying file system. Parallel file systems like Lustre and BeeGFS are optimized for throughput, scalability and bandwidth. They divide data across multiple servers and storage devices to enable concurrent read/write access from multiple nodes. Look for file systems that can deliver over 10 GB/s of aggregate throughput to storage. Additional features like data compression and caching can further boost I/O performance.

### 5. GPU Accelerators

Graphics processing units (GPUs) have become increasingly important for accelerating workloads involving deep learning, computer vision, molecular modeling and more. Top HPC systems are equipped with multiple high-end NVIDIA or AMD GPUs per node for massive parallel processing. Make sure to factor in not just the GPU model but also the connection interface (PCIe or NVLink), the number of GPUs per node, and GPU memory. For deep learning in particular, Volta or Ampere GPUs with at least 32 GB of memory each are a good starting point.

### 6. Specialized Hardware Accelerators

Beyond GPUs, some workloads can further benefit from domain-specific hardware accelerators. These are ASICs purpose-built to accelerate functions like linear algebra, particle simulations, DNA sequencing, etc. Examples include FPGAs, Tensor Cores and AI processors from vendors like Intel, Habana and Xilinx. While such accelerators provide a big boost, their value depends on how well the workload maps to the accelerator's architecture. Carefully evaluate accelerator options against real-world benchmarks for your use cases.

### 7. Dense Node Configuration

HPC systems must pack the maximum computational power into each node so that the calculated data for each node precisely falls within the parallelism and utilized boundaries. Dense configurations featuring up to 8 GPUs and even more than 100 CPU cores on 2–4U rack space are the norm. In this case, workloads are heterogeneous and can use all the resources inside a node before they have to move to other ones.

Secondly, it facilitates spatial and power-efficient systems. Air flow is an indispensable element for tightly packed nodes, so it is important to select cooling systems that will have no dead zones but high airflow throughout the space.

### 8. Scalable Architecture

The capacity to effortlessly move up from testing small groups to a full-scale production deployment is important. Pick specs that come with basic components like standard servers, storage, and a network capable of different combinations. Consider solutions with scale-out architectures instead of the common scale-up SMP approach, if possible.

The system scaling should also be done in a balanced manner, for example, by adding equal amounts of CPUs and GPUs but also not forgetting memory and network bandwidth. The interconnected topologies incorporating high radii and low diameters induce large-scale as well as high-performance computing deployments.

### 9. Resource Management Software

Employing charging and allocation mechanisms for releasing and delivering computing and storing resources when needed. Using industry-standard SLURM, PBS Pro, and LSF for task scheduling are popular performance computing software solutions. Containers, in multi-tenant environments, offer isolation features with enumerators such as singularity and shifter.

Watching tools allow for improved performance through visibility to the system and a clearer view in application profiling. Some [cloud computing](https://www.lenovo.com/de/de/servers-storage/solutions/cloud-computing/) workloads can also handle GPU virtualization of several workloads to concurrently run the data. In the words of the famous author, suitable software will make a system efficient.

### 10. Reliability and Serviceability

Given the scale and value of HPC systems, reliability is paramount. Redundant and hot-swappable components protect against single points of failure. Fault tolerance ensures workloads can resume after unexpected issues. Remote management tools allow administrators to monitor systems and perform maintenance without physical access. Look for systems designed for high availability with 99.999% up-time SLAs. Support contracts provide expert help when needed to minimize downtime for critical workloads.

#### Final Words

Considering all these essential requirements, right from CPUs and memory to networking, storage, software and reliability, helps architect an HPC system truly optimized for demanding workloads. It enables harnessing the full potential of parallel processing across a large cluster. With the right high-performance infrastructure, organizations can accelerate research and innovation through data-centric computing.