Architecture

/sɪmˈdiː/

n. "Single Instruction Multiple Data parallel processing executing identical operation across vector lanes simultaneously."

SIMD is a parallel computing paradigm where one instruction operates on multiple data elements stored in wide vector registers—AVX512 512-bit lanes process 16x FP32 or 8x FP64 simultaneously, accelerating FFT butterflies and matrix multiplies in HPC. CPU vector units like Intel AVX2/ARM SVE2 broadcast scalar opcodes across lanes while masking handles conditional execution without branching.

Key characteristics of SIMD include:

• Vector Widths: SSE 128-bit (4xFP32), AVX2 256-bit (8xFP32), AVX512 512-bit (16xFP32).
• Horizontal Ops: ADDPS/SUBPS/MULPS broadcast across lanes; FMA accelerates BLAS.
• Mask Registers: K0-K7 control per-lane execution avoiding branch divergence.
• Gather/Scatter: Non-contiguous loads/stores for strided access patterns.
• Auto-Vectorization: ICC/GCC -O3 flags detect loop parallelism inserting VMOVDQA.

A conceptual example of SIMD vector addition flow:

1. Load 8x FP32 vectors: VMOVAPS ymm0, [rsi] ; VMOVAPS ymm1, [rdx]
2. SIMD FMA: VFMADD231PS ymm2, ymm1, ymm0 (8 MACs/cycle)
3. Horizontal sum: VHADDPS ymm3, ymm2, ymm2 → reduce across lanes
4. Store result: VMOVAPS [r8], ymm3
5. Advance pointers rsi+=32, rdx+=32, r8+=32
6. Loop 1024x → 8K FLOPS/iteration vs 1K scalar

Conceptually, SIMD is like a teacher grading identical math problems across 16 desks simultaneously—one instruction (add) operates on multiple data (test answers) yielding 16x speedup when problems match.

In essence, SIMD turbocharges HBM-fed AI training and FFT spectrum analysis on SerDes clusters, vectorizing PAM4 equalization filters while EMI-shielded ENIG boards host vector-optimized Bluetooth basebands.

/ˌeɪtʃ piː ˈsiː/

n. "Parallel computing clusters solving complex simulations via massive CPU/GPU node aggregation unlike single workstations."

HPC is the practice of aggregating thousands of compute nodes with high-speed interconnects to perform massively parallel calculations—SerDes 400G fabrics and NVLink 900GB/s link HBM3 memory to 128-GPU SXM blades solving CFD/climate models infeasible on desktops. Exascale systems like Frontier deliver 1.2 exaFLOPS via 3D torus networks where MPI distributes domains across nodes while NCCL handles intra-GPU tensor parallelism.

Key characteristics of HPC include:

• Cluster Architecture: 100K+ nodes with InfiniBand 400Gbps NDR or NVLink domains.
• Memory Bandwidth: HBM3 3TB/s/node feeds FP64 tensor cores for CFD/ML.
• Parallel Frameworks: MPI+OpenMP+CUDA partition domains across socket/GPU/accelerator.
• Scaling Efficiency: 80-95% weak scaling to 100K cores before communication bounds.
• Power Density: 60kW/rack liquid-cooled; PUE <1.1 via rear-door heat exchangers.

A conceptual example of HPC CFD workflow:

1. Domain decomposition: 1B cells → 100K partitions via METIS
2. MPI_Dims_create(1000,100,1) → 3D Cartesian topology
3. Each rank solves 10M-cell NS equations w/ RK4 timestep
4. NVLink halo exchange 1GB/iteration <10μs latency
5. Global residual reduction every 100 steps MPI_Allreduce
6. Checkpoint HBM3 → Lustre 2TB/s every 1000 iterations

Conceptually, HPC is like an ant colony tackling a mountain—millions of tiny processors collaborate via fast chemical signals (SerDes/NVLink) solving problems individually impossible, from weather prediction to PAM4 signal integrity simulation.

In essence, HPC powers exascale science from fusion plasma modeling to Bluetooth 6G PHY optimization, crunching petabytes through DQS-DDR5+HBM3 fed by ENIG backplanes while mitigating EMI in dense racks.