Hardware Selection Consulting is the process of matching "Science to Silicon."

A common mistake in HPC is buying the "fastest" processor on the market, only to discover that your software is bottlenecked by the memory speed, meaning the expensive processor sits idle 50% of the time.

Consulting in this area involves analyzing your specific code (e.g., CFD, AI Training, Molecular Dynamics) to build a Reference Architecture that maximizes performance per dollar.

Here is the detailed breakdown of the selection philosophy, the component landscape, and the decision matrix, followed by the downloadable Word file.

1. The Philosophy: Application-Driven Design

You do not design a supercomputer in a vacuum. You design it around the software it will run.

• Compute Bound Codes (e.g., Crash Simulation): These rely on raw math speed.
• Hardware: High-frequency CPUs (Intel Xeon) with AVX-512 instructions.
• Memory Bound Codes (e.g., Weather, CFD): These are starved for data. The CPU is fast, but RAM is slow.
• Hardware: CPUs with massive memory channels (AMD EPYC) or High Bandwidth Memory (HBM).
• AI & Molecular Dynamics: These are parallelizable.
• Hardware: GPU Acceleration (NVIDIA H100/A100).

2. The Component Landscape

A. The CPU War: Red vs. Blue vs. Green

• AMD EPYC (Genoa/Turin): The current king of core density (up to 96+ cores per chip). Excellent for high-throughput workloads and memory bandwidth.
• Intel Xeon (Sapphire Rapids/Emerald Rapids): Strong in single-core performance and specialized instructions (AMX for AI). Better for legacy code that requires fewer, faster cores.
• ARM (Grace/Graviton): The energy efficiency leader. Becoming popular for cloud-native HPC.

B. The Accelerator (GPU)

• NVIDIA H100: The beast for Large Language Model (LLM) training. Expensive and power-hungry (700W+).
• NVIDIA L40S: A balanced card for graphics, rendering, and lighter AI inference.
• AMD Instinct (MI300): A challenger offering massive memory capacity for scientific simulation.

C. Storage & Interconnect

• Storage: Deciding between NVMe (All-Flash) for speed vs. HDD (Spinning Rust) for capacity.
• Interconnect: Choosing InfiniBand (NDR 400Gb/s) for ultra-low latency vs. Ethernet (RoCE) for compatibility.

3. The Selection Matrix

Application Type	Recommended Hardware Focus	Why?
Generative AI (LLMs)	GPU Dense (NVIDIA H100/H200)	Training requires massive matrix math capabilities.
Fluid Dynamics (CFD)	Memory Bandwidth (AMD EPYC / Intel HBM)	Moving data from RAM to CPU is the bottleneck, not the math.
Finite Element (FEA)	Frequency (High Clock Speed CPUs)	Complex solvers often rely on single-threaded performance.
Genomics	High I/O (NVMe Storage)	DNA sequencing reads/writes millions of small files.