Digital Design & Comp. Arch: L20: GPU Architectures (Spring 2026)
Why It Matters
Understanding GPU architecture’s blend of SIMD, memory banking, and scalar cores helps engineers design systems that avoid bottlenecks and fully exploit parallelism, directly impacting performance and cost in AI and graphics markets.
Key Takeaways
- •GPUs combine array and vector processing for flexible SIMD execution.
- •Memory banking and avoiding bank conflicts are critical for GPU throughput.
- •Modern GPUs integrate scalar cores to mitigate Amdahl’s serial bottleneck.
- •Compiler auto‑vectorization enables massive pixel‑level parallelism in graphics.
- •Historical SIMD extensions (MMX, AVX) paved the way for GPU acceleration.
Summary
The lecture focuses on modern GPU architectures, positioning them as flexible extensions of classic SIMD, array, and vector processors. Building on last week’s SIMD fundamentals, the professor explains how GPUs blend space‑time parallelism, allowing scalar instructions to be dispatched across thousands of threads while retaining vector‑like throughput. Key insights include the central role of memory banking to sustain massive parallel loads, the necessity of avoiding bank conflicts, and the integration of scalar execution units to address Amdahl’s law‑driven serial bottlenecks. The discussion also highlights automatic code vectorization as a practical pathway for turning pixel‑wise graphics loops into highly parallel GPU kernels. Illustrative examples reference Bob Brow’s seminal 35‑year‑old paper on VIW architectures, Cray’s early memory‑banking strategy, and Intel’s controversial MMX rollout, which ultimately seeded today’s AVX extensions and GPU‑friendly instruction sets. The professor underscores how compiler analysis determines vectorizability, using image‑processing loops as a canonical case. For practitioners, the lecture signals that future GPU designs must co‑optimize memory hierarchy, bank‑conflict mitigation, and scalar performance. It also suggests that continued ISA evolution and compiler sophistication will be decisive in extending GPU applicability beyond graphics into broader AI and high‑performance workloads.
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