Parallel

/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.

/ˌdʒiː-piː-dʒiː-piː-juː/

n. “The use of a graphics processing unit to perform general-purpose computation.”

GPGPU, short for General-Purpose computing on Graphics Processing Units, refers to using a GPU to perform computations that are not limited to graphics rendering. While GPUs were originally designed to accelerate drawing pixels and polygons, their massively parallel architecture makes them exceptionally good at handling large-scale numerical and data-parallel workloads.

Traditional CPUs are optimized for low-latency, sequential tasks, handling a small number of complex threads efficiently. In contrast, GPGPU exploits the fact that many problems can be broken into thousands or millions of smaller, identical operations and processed simultaneously across GPU cores.

Key characteristics of GPGPU include:

• Massive Parallelism: Thousands of lightweight threads execute the same instruction across different data.
• High Throughput: Optimized for moving and processing large volumes of data quickly.
• Compute Kernels: Programs are written as kernels that run in parallel on the GPU.
• Specialized APIs: Commonly implemented using CUDA, OpenCL, or Vulkan compute shaders.
• CPU Offloading: Frees the CPU to manage control logic while heavy computation runs on the GPU.

Conceptual example of a GPGPU workflow:

// Conceptual GPGPU execution
CPU prepares data
CPU launches GPU kernel
GPU processes data in parallel
Results copied back to system memory

GPGPU is widely used in scientific simulation, machine learning, cryptography, video encoding, physics engines, and real-time data analysis. Many modern AI workloads rely on GPGPU because matrix operations map naturally onto GPU parallelism.

Conceptually, GPGPU is like replacing a single master craftsman with an army of specialists, each performing the same small task at once. The individual workers are simple, but together they achieve extraordinary computational power.

In essence, GPGPU transforms the GPU from a graphics-only device into a general-purpose accelerator, reshaping how high-performance and data-intensive computing problems are solved.