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This week’s system design refresher:
CPU vs GPU vs TPU
How OAuth 2 Works
How Distributed Tracing Works at the High Level?
How GPUs Work at a High Level
Top 4 API Gateway Use Cases
CPU vs GPU vs TPU
Why does the same code run fast on a GPU, slow on a CPU, and leave both behind on a TPU? The answer is architecture. CPUs, GPUs, and TPUs are designed for different workloads.
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CPU (Central Processing Unit): The CPU handles general-purpose computing. It's built for low latency and complex control flow, branching logic, system calls, interrupts, and decision-heavy code.
Operating systems, databases, and most applications run on the CPU because they need that flexibility.
GPU (Graphics Processing Unit): GPUs work differently. Instead of a few cores, they spread the work across thousands of cores that execute the same instruction across huge datasets (SIMT/SIMD-style).
If your workload is repetitive like matrix math, pixel shading, tensor operations, GPUs handle it quickly.
TPU (Tensor Processing Unit): TPUs are specialized hardware. The architecture is designed around matrix multiplication using systolic arrays, with compiler-controlled dataflow and on-chip buffers for weights and activations.
They are fast at neural network training and inference, as long as the workload fits the hardware well.
Over to you: When designing systems today, how do you decide what runs on CPU vs GPU vs specialized accelerators?
How OAuth 2 Works
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Authorization Code Flow (+ PKCE) - for user login:
User requests a protected resource
Server redirects to the Authorization Server (IdP)
Client generates a code_verifier and code_challenge (PKCE)
User authenticates and gives consent
IdP returns an authorization code
Server exchanges the code (with the verifier) for tokens
Server validates tokens and creates a session
PKCE prevents intercepted authorization codes from being reused. That’s why it’s the modern default for web and mobile apps.
Client Credentials Flow - for service-to-service: