Architecture

/dɪˈstrɪbjʊtɪd ˈsɪstəmz/

noun … “Independent computers acting as one system.”

Distributed Systems are computing systems composed of multiple independent computers that communicate over a network and coordinate their actions to appear as a single coherent system. Each component has its own memory and execution context, and failures or delays are expected rather than exceptional. The defining challenge of distributed systems is managing coordination, consistency, and reliability in the presence of partial failure and unpredictable communication.

At a technical level, distributed systems rely on message passing rather than shared memory. Components exchange data and commands using network protocols, often through remote procedure calls or asynchronous messaging. Because messages can be delayed, reordered, duplicated, or lost, system behavior must be designed to tolerate uncertainty. This sharply distinguishes distributed systems from single-machine Concurrency or shared-memory Parallelism, where communication is faster and more reliable.

A central concern in distributed systems is consistency. When data is replicated across nodes for availability or performance, the system must define how updates propagate and how conflicting views are resolved. Some systems favor strong consistency, ensuring all nodes observe the same state at the cost of latency or availability. Others favor eventual consistency, allowing temporary divergence while guaranteeing convergence over time. These tradeoffs are formalized by the CAP Theorem, which states that a distributed system cannot simultaneously guarantee consistency, availability, and partition tolerance.

Fault tolerance is another defining characteristic. Individual machines, network links, or entire regions can fail independently. Distributed systems are therefore designed to detect failures, reroute requests, and recover state automatically. Techniques such as replication, leader election, heartbeats, and consensus protocols enable systems to continue operating even when parts of the system are unreachable. These mechanisms are complex because failures are often indistinguishable from slow communication.

In practice, distributed systems appear in many forms. A web application may run across multiple servers behind a load balancer, each handling requests independently while sharing data through distributed storage. A cloud platform coordinates compute, storage, and networking across data centers. Large-scale data processing frameworks divide workloads across clusters and aggregate results. In each case, the system is designed so users interact with a single logical service rather than many separate machines.

A typical workflow example involves a distributed database. Client requests are routed to different nodes based on data location or load. Writes may be replicated to multiple nodes for durability, while reads may be served from the nearest replica for performance. Background processes reconcile replicas to ensure convergence. Throughout this process, the system must balance latency, throughput, and correctness while handling failures transparently.

Designing distributed systems requires abandoning assumptions that hold on a single machine. There is no global clock, network communication is unreliable, and failures are inevitable. Successful designs embrace these realities by favoring idempotent operations, explicit timeouts, retries, and well-defined consistency models.

Conceptually, distributed systems are like an orchestra without a single conductor. Each musician listens, adapts, and follows shared rules. When coordination succeeds, the result sounds unified. When it fails, the cracks reveal just how hard cooperation becomes at a distance.

See Concurrency, Parallelism, CAP Theorem, Actor Model, Consensus.

/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.