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Privacy Policy

Data Protection & Cookie Governance

Privacy Policy

Effective Date: October 1, 2026 • Last Reviewed: October 17, 2026

At Global Tech Digest (https://globaltechdigest.blogspot.com/), protecting the privacy and personal data of our readers, engineers, and subscribers is a foundational operational priority. This Privacy Policy details the types of information collected when you visit our publication, how that information is processed, and your statutory rights under international data protection frameworks including the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA/CPRA).

1. Information We Collect

We distinguish between technical telemetry collected automatically by web infrastructure and information you voluntarily provide:

  • Voluntary Correspondence: When you contact our editorial team via email (ossam.hashim@gmail.com) or submit technical errata, we receive your name, email address, and the contents of your message solely to respond to your inquiry.
  • Client-Side Engineering Calculators: All inputs entered into our interactive engineering utilities (such as the LLM VRAM, KV-Cache & GPU Power Calculator) are processed 100% locally inside your browser via client-side JavaScript and are never transmitted to external servers.
  • Standard Server Log Files: Like all web publications hosted on Google Blogger infrastructure, standard server logs record non-identifying technical metadata including Internet Protocol (IP) addresses, browser user-agent strings, Internet Service Provider (ISP), referring/exit pages, operating system platform, and date/time stamps. This telemetry is used strictly to diagnose rendering issues, prevent automated abuse, and analyze aggregate readership trends.

2. Google AdSense & The DoubleClick DART Cookie

Global Tech Digest participates in the Google AdSense program to support independent technical research and open-access engineering publications:

  • Third-Party Vendor Disclosure: Google, as a third-party vendor, uses cookies to serve advertisements on https://globaltechdigest.blogspot.com/.
  • DART Cookie Mechanics: Google's use of advertising cookies (including the DoubleClick DART cookie) enables it and its partners to serve ads to our readers based on their visit to Global Tech Digest and/or other websites on the Internet.
  • How to Opt Out of Personalized Advertising: Readers may opt out of personalized advertising at any time by visiting Google Ads Settings or by reviewing the Google Privacy & Terms for Advertising Technologies. Alternatively, you can opt out of third-party vendor cookies for personalized advertising by visiting www.aboutads.info.

3. Cookies, Web Beacons & Third-Party Links

Our articles frequently cite external primary sources, including GitHub repositories, arXiv preprints, W3C/IETF standards, and hardware vendor documentation. Global Tech Digest has no control over, and assumes no responsibility for, the privacy practices or cookies deployed by external third-party domains. You can choose to disable cookies selectively through your individual browser settings.

4. GDPR & CCPA Data Protection Rights

Depending on your jurisdiction, you hold the following statutory rights regarding your personal information:

  • The Right to Access, Rectification, and Erasure: You may request copies of any personal correspondence held by us, request correction of inaccurate data, or request permanent deletion of your email records.
  • The Right to Restrict Processing & Opt Out of Sale/Sharing: Global Tech Digest does not sell personal contact lists to third parties under any circumstances.

5. Contact Our Data Protection & Editorial Team

If you have any questions about this Privacy Policy or wish to exercise your data protection rights, please contact our Principal Editor directly at ossam.hashim@gmail.com.

Comments

Kernel Engineering Under Physical Non-Idealities: Floorsweeping, Wave-Equalized Launches, and the DVFS Confounder Behind H100 Clustering Gains

GPU SYSTEMS An NUS team shows the H100 you programmed is not the H100 you have: fabrication binning quietly removes whole SMs from cluster reach, and power limits rewrite the speedup math. Accounting for both recovers up to 5.30% on FlashAttention 4. By Ossam Hashim KEY FINDINGS Floorsweeping, the binning step that disables defective silicon to salvage a die, is invisible to the ISA but visible to cluster placement: on the H100 SXM5 it removes GPC-level clustered execution capacity, cutting SMs reachable by clusters from 132 to 124 (paper's measurement). Cluster placement also drifts at runtime: the paper's microbenchmark records only 120 unique SM IDs per kernel iteration but 124 across iterations, so the effective SM budget oscillates launch to launch. Fewer usable SMs can add a full execution wave: a launch that needs one wave at 132 SMs can need two at 120, erasing the data-movement savings DSMEM clustering was supposed to buy. The fix is arithmetic,...

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