High Powered Computing
This is the set of processes involved in accelerating computation processes, through distribution and parallelization of the kernels.
NVIDIA is a major player in this market.
Design Practice
CUDA
For CUDA, NVIDIA suggests making use of the APOD design cycle, which have the following stages:
- Assess
- Parallelize
- Optimize
- Deploy
Hardware Technologies Behind HPC
High-Performance Processors
Multi-core CPUs → Modern HPC clusters use CPUs with dozens of cores (AMD EPYC, Intel Xeon) for general-purpose tasks.
GPUs (Graphics Processing Units) → Massively parallel processors (NVIDIA A100/H100, AMD Instinct) optimized for floating-point and tensor operations.
Accelerators
- AI/ML chips → Tensor Cores (NVIDIA), TPUs (Google).
- FPGAs (Field Programmable Gate Arrays) → Custom accelerators for specialized workloads.
Impact: Provides raw compute power for simulation, modeling, AI, and scientific workloads.
Memory Technologies
- DRAM (High-capacity main memory) → For storing working sets of data.
- HBM (High Bandwidth Memory) → Stacked memory with huge bandwidth (used in NVIDIA GPUs, AMD Instinct).
- GDDR (Graphics DDR) → High-speed GPU memory for throughput.
- NVRAM / Persistent Memory (e.g., Intel Optane) → Bridges gap between RAM and storage.
- Cache Hierarchies (L1/L2/L3) → Reduce latency in CPU/GPU data access.
Impact: Enables fast data access and minimizes bottlenecks in parallel workloads.
High-Speed Interconnects
- InfiniBand → Low-latency, high-bandwidth networking for HPC clusters (Mellanox/NVIDIA).
- NVLink / NVSwitch → NVIDIA’s GPU-to-GPU high-bandwidth interconnect.
- PCI Express (PCIe Gen5/Gen6) → Standard CPU↔GPU and peripheral communication bus.
- CXL (Compute Express Link) → Emerging standard for coherent memory sharing across CPUs/accelerators.
- Ethernet (100G/200G/400G) → Still widely used in HPC and hyperscale data centers.
Impact: Critical for scaling HPC across thousands of nodes with minimal communication overhead.
Storage Systems
- Parallel File Systems → Lustre, IBM Spectrum Scale (GPFS), BeeGFS → designed for massive throughput.
- NVMe SSDs → High-speed local storage for compute nodes.
- Burst Buffers → Fast SSD caches to absorb I/O spikes between compute and storage layers.
- Object Storage → Scalability for unstructured scientific data.
Impact: Efficiently handles terabytes–petabytes of data generated by simulations and AI workloads.
Energy & Cooling Solutions
- Liquid Cooling (direct-to-chip, immersion cooling) → Needed for dense GPU clusters.
- Efficient Power Delivery → Optimized PSUs and voltage regulators.
- Thermal-aware architectures → Helps achieve performance without thermal throttling.
Impact: Keeps supercomputers efficient and sustainable at megawatt-scale power.
System Integration & Architecture
- Clustered Supercomputers → Thousands of nodes connected via high-speed networks.
- Heterogeneous Computing → CPUs + GPUs + FPGAs working together.
- Exascale Systems → Modern HPC targets >10^18 FLOPS (e.g., Frontier, Aurora, El Capitan).
- Node-level Innovations → Dense GPU servers (e.g., NVIDIA DGX, AMD Instinct MI300-based systems).
Impact: Scales from single-node HPC servers to the world’s fastest supercomputers.