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Today I'm excited to share the project a small team of amazing people and I have been working on for the past two years; HipHop for PHP. With HipHop we've reduced the CPU usage on our Web servers on average by about fifty percent, depending on the page. Less CPU means fewer servers, which means less overhead. This project has had a tremendous impact on Facebook. We feel the Web at large can benefit from HipHop, so we are releasing it as open source this evening in hope that it brings a new focus toward scaling large complex websites with PHP. While HipHop has shown us incredible results, it's certainly not complete and you should be comfortable with beta software before trying it out. HipHop for PHP isn't technically a compiler itself. Rather it is a source code transformer. HipHop programmatically transforms your PHP source code into highly optimized C++ and then uses g++ to compile it. HipHop executes the source code in a semantically equivalent manner and sacrifices some rarely used features — such as eval() — in exchange for improved performance. HipHop includes a code transformer, a reimplementation of PHP's runtime system, and a rewrite of many common PHP Extensions to take advantage of these performance optimizations.

The first textbook of its kind, Programming Massively Parallel Processors: A Hands-on Approach launches today, authored by Dr. David B. Kirk, NVIDIA Fellow and former chief scientist, and Dr. Wen-mei Hwu, who serves at the University of Illinois at Urbana-Champaign as Chair of Electrical and Computer Engineering in the Coordinated Science Laboratory, co-director of the Universal Parallel Computing Research Center and principal investigator of the CUDA Center of Excellence. The textbook, which is 256 pages, is the first aimed at teaching advanced students and professionals the basic concepts of parallel programming and GPU architectures. Published by Morgan Kaufmann, it explores various techniques for constructing parallel programs and reviews numerous case studies. With conventional CPU-based computing no longer scaling in performance and the world’s computational challenges increasing in complexity, the need for massively parallel processing has never been greater. GPUs have hundreds of cores capable of delivering transformative performance increases across a wide range of computational challenges. The rise of these multi-core architectures has raised the need to teach advanced programmers a new and essential skill: how to program massively parallel processors.

Today face.com is proud to announce the opening of our platform APIs! After scanning billions of photos and tagging over 50 million users through Photo Tagger and Photo Finder, we’re moving ahead with our goal of making face recognition approachable and available to all. In this open alpha stage, we’re letting any developer tap into our face detection and face recognition tech through simple REST API calls. Whether you’re looking to build a cool photo tagging application, create personalized e-cards or campaigns, or any other sci-fi idea that comes to mind, we’re here to serve. A friend of the company, world-famous programmer, developer and founder of Technorati David Sifry got an early look at our API.  In David’s own words: “I’ve been impressed with Face.com’s API, and their plan for working closely with developers to build great applications that incorporate face detection and face recognition. Open platforms like this one will enable the creation of exciting new applications that we’ve never seen before at scale.”