Thursday, July 30, 2026

SIMD: Single instruction, multiple data

Single instruction, multiple data - Wikipedia

 SIMD describes computers with multiple processing elements that perform the same operation on multiple data points simultaneously.


 Everyone Should Know SIMD – Mitchell Hashimoto 

SIMD (Single Instruction, Multiple Data) has an undeserved reputation for being overly complex or reserved strictly for high-performance niche applications. Most everyday "process $N$ values at a time" SIMD code follows a simple, predictable pattern that any software engineer can easily adopt to achieve significant speedups (often 4x–16x).


The 5-Step "Common Shape" of SIMD Code

Most standard SIMD loops can be broken down into five distinct steps:

  1. Broadcast Constants & Setup: Prepare the vector type based on the CPU’s target lane count (e.g., 4, 8, or 16 values) and broadcast constant comparison or arithmetic values into every vector lane (using operations like @splat).

  2. Loop Vector-Width Chunks: Load input elements into full vector-sized chunks rather than processing them one-by-one.

  3. Perform the SIMD Operation: Apply parallel operations across all vector lanes simultaneously with a single instruction (e.g., comparing loaded vector data against the constant vector).

  4. Reduce the Vector Result: Transform the parallel vector results into a scalar outcome (such as using @reduce, bit masks, or trailing zero counts (@ctz) to find specific indices or aggregate sums).

  5. Scalar Tail Fallback: Process any remaining elements (that don't fit into a full vector chunk) using a traditional scalar loop. This tail loop also acts as the fallback for unsupported CPUs.


Key Takeaways

  • Why Manual SIMD Over Compiler Auto-Vectorization? While modern compilers attempt auto-vectorization, they frequently miss opportunities due to complex control flow. Explicit SIMD code guarantees consistent, predictable performance across compiler updates.

  • Accessibility: Using generic vector support in modern languages (such as Zig) eliminates the need for CPU-specific assembly or complex intrinsic functions.

  • Rule of Thumb: If you are iterating over contiguous data (e.g., bytes, strings, arrays) with regular operations across hundreds or thousands of items, it is worth structuring the logic using the SIMD pattern.


AI: free knowledge (models), expensive "books" (data centers)?

Apparently only Google is doing this "right", "Apple style" designing and controlling whole "stack" from HW, AI models and AI enabled apps that could be monetized.

Are Anthropic, OpenAI, and almost half of US stock market that depends on them in trouble,
thanks to Chinese "clone" open AI models?

This may be a very, very serious question... 

Short term profit ("free" models) may cost very significantly even on short term, leave alone on long term.


YouTube video titled "I'm disappointed" by Matthew Berman:


📌 Main Takeaways

  • Debate on Open vs. Closed AI Models: The video dives into the ongoing tension between closed-system AI providers (like Anthropic) and the open-source community.

  • Anthropic & Distillation Restrictions: Discussion centers around Anthropic's policies and stance regarding model distillation and access restriction due to safety/existential risk concerns, which many in the open-source community view as anti-competitive or paranoid.

  • Open-Source Resilience: Highlighting instances where open-source AI models stepped in to provide transparency, accessibility, and community protection when closed systems locked down their models.

  • Value Shift in AI: The idea that long-term value in the AI ecosystem may shift away from base model ownership toward applications, user experience, and integration layer products.


💬 Viewer Sentiment

Top comments reflect strong support for open-source AI as a public good, expressing frustration over closed-source monopolies restricting access under the banner of safety.


 Dario vs Jensen on Open Weights, OpenAI & Anthropic in DC, Xi Exports AI to Global South | EP #275 - YouTube

This episode of Moonshots with Peter Diamandis explores critical developments shaping the artificial intelligence landscape, focusing on high-stakes industry debates over open vs. closed AI models, government policy and lobbying efforts in Washington, D.C., and international geopolitical developments in AI deployment.

  • Open vs. Closed AI Debate (Dario Amodei vs. Jensen Huang):

    • The hosts dissect the ongoing debate between Anthropic CEO Dario Amodei and NVIDIA CEO Jensen Huang regarding open-weight models versus proprietary closed models.

    • Highlighted in the discussion is Jensen Huang's push for the Open Secure AI Alliance, emphasizing how open-weight frontier models play a critical role in cybersecurity defense and global collaboration alongside closed models.

  • D.C. Policy & Lobbying Dynamics:

    • OpenAI & Anthropic Alliance: A focus on top AI frontier labs (OpenAI and Anthropic) joining forces to lobby lawmakers in Washington, D.C., aiming to influence regulatory frameworks, safety standards, and AI infrastructure policy.

  • Global AI Strategy & Expansion:

    • Kimi K3 & Global Expansion: Discussion around Chinese AI models (such as Moonshot AI's Kimi K3) and China's strategy to export AI technology to the Global South to build international influence and infrastructure.