GPU and CPU: Which Is Better for HPC?

High-performance computing (HPC) refers to computing systems and environments that typically utilise many processors (as part of a single machine) or several computers organised within a cluster (operating as a single computing resource). As core components of these computing systems, GPUs and CPUs play a crucial role. Understanding the concepts, differences, and roles of GPUs and CPUs is essential for determining which is more suitable for specific HPC tasks.

GPU and CPU

What Is GPU?

A GPU (Graphics Processing Unit) is a processor specialised in handling graphics and image computations. Initially designed for graphics rendering and display, GPUs are now used by individuals and enterprises for extensive mathematical calculations, training and deploying machine learning (ML) systems, and conducting large-scale data analysis across various industries.

GPUs typically consist of hundreds or even thousands of cores, providing unprecedented parallel processing capabilities. Unlike CPU cores, which allocate processing time to multiple tasks sequentially based on a preemption mechanism, all GPU cores simultaneously execute the same mathematical instructions on different datasets.

Relying solely on CPUs for real-time data analysis, blockchain verification, ML training, neural network applications, and similar tasks often leads to bottlenecks and delivery delays. Consequently, enterprises have begun to demand GPU-assisted HPC setups for these workloads.

What Is CPU?

A CPU (Central Processing Unit) is the 'heart' of computers, mobile phones, and many other electronic devices. In numerous everyday scenarios, such as watching videos, playing games, and interacting online, the CPU plays a crucial role in unified command and dispatch. It is primarily responsible for executing program instructions, performing arithmetic and logical operations, and controlling and coordinating the various components of the computer.

Every computer's core is the CPU, whether single-core or multi-core. HPC systems also rely on CPUs to handle initial operations and manage core functions such as executing operating system instructions, fetching data from RAM and cache memory, directing data flow to the bus, and managing interconnect resources.

All preliminary operations are handled by the CPU. In HPC applications, the CPU provides precision for large-scale computing and directs other components, including GPUs, to perform complex and resource-intensive computations.

Comparison of GPU and CPU in HPC

In an HPC cluster, both the GPU and CPU have their distinct responsibilities. The CPU remains the crucial processor for running the operating system and other applications such as firewalls within the HPC cluster. Just as a computer cannot function without a CPU (the main processor), an HPC cluster cannot operate without one either.

GPUs are powerful processors with multiple cores, capable of handling large, complex computations with ease. With the support of the CPU and carefully crafted instruction sets, GPUs can coordinate the entire HPC cluster, acting as hardware accelerators to enhance resource-intensive tasks. These tasks form the foundation of AI/ML development, NLP, ANN, and other high-performance computing applications. While it is possible to design an HPC cluster without GPUs, it would ultimately prove insufficient for handling heavy computational tasks.

Therefore, it is evident that both the CPU and GPU are essential for running an HPC system. Without a CPU, we cannot run an HPC system. Conversely, without a GPU, the high-performance computing system would be unable to achieve optimal performance.

For further information, also check: Deep Comparison Between Server CPU and GPU

How do HPC Systems Utilise CPU and GPU?

The two primary methods of processing information in HPC are as follows:

• Serial Processing: This method is used by CPUs. Typically, each CPU core processes one task at a time. Running the operating system, basic applications such as office productivity and word processing require the use of CPUs.

• Parallel Processing: - This method is executed by multiple CPUs or GPUs. GPUs can perform multiple arithmetic operations on data matrices simultaneously. This design enables GPUs to work on multiple data planes concurrently.

Most HPC systems achieve parallel processing by aggregating multiple processors and memory modules through ultra-high bandwidth interconnects. Some HPC systems offer a combination of GPUs and CPUs, known as heterogeneous computing.

Manage HPC Systems Using FS GPU and CPU

FS offers powerful solutions for both GPU and CPU-based HPC systems, tailored to meet the demands of various applications, from deep learning to complex data analysis. The FS GPU and CPU servers, RS6460 and RS3110 respectively, are designed to deliver exceptional performance, flexibility, and scalability.

FS GPU

The RS6460 is a 4U dual-socket rack-mounted accelerated computing GPU server. It supports up to 24 DDR4 memory modules, up to 24 hot-swappable SAS/SATA HDDs, and a variety of network options, ensuring high performance and flexible I/O for diverse applications. This next-generation server is ideal for HPC, deep learning, and training large language models.

• 2x 2nd Gen Intel® Xeon® Scalable Processors with up to 52 Cores

• Support up to 8 FHFL Double-Width/Single-width PCIe GPUs

• Up to 24 DIMM slots for a Maximum of 6TB with 2933MHz ECC DDR4 LRDIMM

• Up to 24x 3.5''/2.5'' Hot-swap SAS/SATA/SSD Drive Bays

• Easily Expand with up to 11 PCIe3.0 Slots

• Support RAID 0, 1, 5, 6, 10, 50, 60, JBOD

FS CPU

The RS3110 is an HPC server featuring the Intel C246 chipset. It supports 8th and 9th gen Intel® Core™ i3/i5/i7/i9 series and Intel® Xeon® E-2100/2200 series processors. The CPU includes integrated core graphics with hardware decoding and up to 1080P transcoding capability.

• Support for Single Socket Intel® Xeon-E-2100/2200 Series Processor

• 4 DIMM Slots - Up to 128GB ECC RAM, DDR4-2666MHz

• 4x 3.5''/2.5'' SAS/SATA/SSD Hot-swappable Drive Bays

• Easily expandable with 1 PCIe 3.0 Slot

• Supports Operating Systems: Windows, Redhat, and Ubuntu OS

Conclusion

It's clear that both GPUs and CPUs are essential for achieving optimal performance in HPC systems. They must work together to support accelerated performance across workloads. The CPU manages operating system instructions, hardware, and I/O operations, while the GPU facilitates the operations that enable the HPC system to deliver its intended workload.

For most HPC applications, a hybrid approach that utilises both GPUs and CPUs is the optimal solution. FS provides top-tier products, services, and tailored solutions for HPC, data centres, and enterprises. We offer exceptional performance at highly competitive prices, ensuring your business remains at the forefront of the industry.