CPU Core Count vs Clock Speeds

How Core Count and Clock Speed Impact Performance

The CPU, or central processing unit, is the brain of the computer, defined by 2 performance characteristics: Core Count and Clock Speed. For consumer processors like AMD Ryzen and Intel Core, choosing is straightforward: higher-tier models offer more cores and faster clock speeds. However, workstation processors (AMD Threadripper, Intel Xeon W) and server processors (AMD EPYC, Intel Xeon 6) present a more complex landscape, with specifications tailored to specific workloads.

Both core count and clock speed significantly impact application performance and efficiency. Understanding their trade-offs is essential for deciding which processor best suits your needs.

Think of core count and clock speed like a team of workers: core count represents the number of workers available, while clock speed determines how quickly each worker completes their individual tasks.

Core Count - Parallel Processing Power

Core count refers to the number of independent processing units in your CPU. Each core can execute instructions simultaneously, enabling parallel processing.

Key benefits of higher core counts:

• Handle multiple tasks at once
• Essential for parallelizable HPC applications like data analytics and cloud virtualization
• More cores = more simultaneous workers completing independent tasks

Limitations:

• Single-threaded workloads don't benefit from additional cores
• Sequential tasks (where each step depends on the previous one) cannot be accelerated with more cores

Example: In AI neural network training, updating weights in a single forward pass involves independent calculations that multiple cores can go ahead and solve in parallel. However, some workloads, calculations are done one after another or sequentially—extra cores sit idle, and the only way to speed up completion is faster execution per core.

Clock Speed - Sequential Task Performance

Clock speed, measured in gigahertz (GHz), determines how fast each core executes instructions. Higher clock speeds mean faster completion of individual tasks. All workloads will want the highest clock speed if possible. But in reality, the trade-off for additional cores is to lower the clock speed or to increase power (and thus cooling). Unfortunately, we have to choose.

When clock speed matters most:

• Sequential or single-threaded workloads
• Applications that cannot leverage parallel processing to a large extent
• HPC simulations where calculations depend on previous results

Example: In particle simulations, one particle's new position affects subsequent calculations—this sequential dependency limits parallelization. Higher clock speeds directly reduce the time to complete each computational step, accelerating the entire workflow.

In the AI neural network analogy, the faster our cores complete a forward pass, the faster they can advance to the next pass, improving overall throughput. In our AI training example, we want high-clock speed to do jobs faster, but enough CPU cores to work in parallel so they can all move onto the next step.

GPUs - Massively Parallel Architecture

GPUs take a fundamentally different approach: extreme core counts (1000x more than CPUs) with lower individual clock speeds.

Why GPUs excel at parallel workloads:

• Architecture specifically designed for parallel computing
• Break problems into thousands of independent operations
• Execute simultaneously across massive core arrays

Ideal GPU workloads:

• Graphics rendering and pixel processing
• Matrix multiplications in neural networks
• Fluid dynamics calculations
• Any task divisible into thousands of independent operations

This massive parallelism enables GPUs to process properly parallelized workloads at speeds CPUs cannot match, making them indispensable for modern AI, simulation, and rendering applications.

More Cores or More Clock Speed?

CPU manufacturers continue pushing density limits. AMD's EPYC 9965 offers 192 cores, making it one of the densest x86 processors available. More cores enable parallel processing, where tasks are divided and executed simultaneously.

More isn't Always Better

However, more cores aren't universally better. High core count processors like the 128-core AMD EPYC 9755 run at lower base clocks (around 2.70GHz). But it isn’t made for per-core speed; it is made with Cloud Native and Virtualization workloads and data center density in mind.

• High core count CPUs are designed for cloud providers distribute groups of cores for light cloud workloads such as fetching data, web apps, hosting, and microservices, which aren’t computationally heavy.
• High clock speed CPUs are designed for HPC workload for CPU-dependent computationally heavy tasks like CPU-rendering, FEA simulation, and more. These low-clock speed cores would struggle to keep up, leaving valuable time on the table.

To achieve optimal performance in any workload, balance core count and clock speed, and determine the ideal configuration that addresses needs. A balanced approach is necessary in CPU-based rendering, choosing a processor with a moderate to high core count and a relatively high clock speed. We will go over the general recommended CPUs for each workload.

Per-Core CPU Licensing

Some applications also price their licensing model on a per-core basis; For Ansys, enabling more cores requires purchasing additional licensing packages. Choose processors with the highest clock speeds to maintain competitive leadership while keeping costs to a minimum.

GPU Native vs GPU Accelerated Workloads

Exxact specializes in GPU-accelerated computing for data centers and enterprise workstations. NVIDIA pioneered GPU parallel processing—originally developed for gaming—and applied it to HPC workloads like simulation and deep learning. Modern breakthroughs in AI, drug discovery, and engineering simulation rely on GPU parallel computing.

CPUs have limited cores designed for computing complex tasks, whereas GPUs are strictly tasked with math calculations.

GPU Native

In GPU native applications, all calculations are offloaded to the GPU, with the CPU cores left idle. GPU native applications like AMBER for molecular dynamics, Ansys Fluent for CFD simulation, and training AI all use predominantly GPU computing and don’t rely too much on the CPU. High clock speed CPU is preferred but a high core count CPU fits for those doing multi-instance GPU setups.

Key characteristics of GPU native applications:

• All calculations offloaded to the GPU
• CPU cores remain idle except for data retrieval and export
• The performance bottleneck is data transfer speed, not core count
• High clock speed CPUs optimize data handling efficiency

GPU Accelerated

However, some workloads are GPU-accelerated (as opposed to native) and utilize the GPUs in certain processes in a workload while still relying on CPUs other parts of the computation. This includes workloads like Finite Element Analysis or Data Analytics, which both need to process data, run calculations, and sequentially analyze all data while offloading a couple parallelizable steps in the job.

Key characteristics of GPU-accelerated applications:

• GPUs handle specific parallelizable computations while CPUs process sequential tasks
• Both CPU and GPU actively contribute to the workload
• Performance depends on both core count for parallel processing and clock speed for sequential operations
• Balanced CPU configurations optimize the interplay between CPU and GPU workloads

Exxact has worked with thousands of customers over our lifespan, encountering numerous workloads. That’s why we offer custom configurable solutions to increase productivity, inspire creativity, and fuel innovation for any type of computing. Our sales engineers are here to help configure the right system for your workload.

Choosing the CPU for Certain HPC Workloads

Certain applications and workloads benefit from both a high clock speed and an ample number of cores. Assuming your system is GPU-equipped, here are the suggested recommendations on what to prioritize for your CPU. The list goes off of base clock speeds:

CPU Recommendation Matrix

Workload and CPU Option Matrix

CPU for Molecular Dynamics & Cryo-EM

For GPU-native applications like AMBER or GROMACS, 2-4 CPU cores per GPU are sufficient—prioritize high clock speeds for efficient data handling.

For GPU-accelerated and high data-intensive workloads like Cryo-EM, choose a processor with balanced cores and clock speed. The high clock speed up data transfer speeds while ample cores are needed for the 2D classification and 3D reconstruction job.

• Recommendation: Opt for Balanced with emphasis on High Clock Speed CPUs.

CPU for FEA Engineering Simulation

In finite element analysis, the CPU handles most computational work due to the sequential nature of mechanical deformation calculations. A balanced core count with high clock speed delivers optimal performance, as fast cores should be prioritized even though additional cores can provide acceleration.

• Recommendation: Opt for Balanced with emphasis on High Clock Speed CPUs. Core count depends on per-core licensing.

CPU for CFD Engineering Simulation

GPU solvers in computational fluid dynamics are drastically more performant than CPU solvers—a single GPU can match the power of 100 CPU cores, accelerating simulations by over 10x. When running GPU-native CFD, prioritize higher clock speeds. For CPU-only CFD simulation, prioritize a high core count. Consider other simulation workloads on the same system that may require balanced CPU configurations.

• Recommendation 1 (CFD with GPU-Native): Opt for High Clock Speed CPUs
• Recommendation 2 (CFD for CPU-Only): Opt for High Core Count configurations (with per-core licensing consideration)

CPU for AI Training and Inferencing

AI training is highly parallelized and distributed, making core count the primary consideration. More CPU cores enable the server to handle more simultaneous tasks, improving scalability and enabling training of larger models. For example, a server with 8 GPUs should have 32+ cores to handle data processing overhead. Decent clock speeds still contribute to faster data processing.

• Recommendation: Opt for High Core Count CPUs.

CPU for Video & 3D Rendering

Prioritize clock speeds while maintaining ample cores. Higher clock speeds improve responsiveness in editing software and speed up real-time previews, preventing stuttering during real-time viewing. Additional cores accelerate exporting, encoding, and final rendering tasks.

• Recommendation: Opt for Balanced with emphasis on High Core Count CPUs.

CPU for HPC Cloud Services and Virtualization

Maximizing core count enables more cloud instances and virtualized web applications to run simultaneously. Each core can support independent service instances. If virtualization clients run compute-intensive workloads, clock speeds should also be considered.

• Recommendation: Opt for High Core Count CPUs.

Conclusions

It's important to consider that the ideal balance between clock speeds and core count can vary depending on the specific workload and software optimization. Different applications have different requirements, and it's crucial to assess the workload characteristics to determine the optimal configuration. These processors are suggestions to help guide you in the right direction and educate you in choosing the right processor for your workload.

It is good practice to check benchmarks, read the documentation, talk to application experts, and, of course, ask a professional like our team at Exxact. At Exxact, our team has not only encountered all kinds of workloads, but we configured systems to run their workload optimally and efficiently.