The New Parallel Forall

Parallel_ForAll_F_wht_V_green_gradient_340x190Today I’m excited to introduce you to the new and improved Parallel Forall blog. For over a year and a half, Parallel Forall has been a part of NVIDIA’s CUDA Zone website, but to better serve the parallel programming community, today we’re launching a dedicated home for Parallel Forall at http://devblogs.nvidia.com/parallelforall.

In addition to a fantastic new design (including a mobile-friendly theme), the new Parallel Forall adds a host of great features that readers expect from a great blog. The most exciting of these are features that allow readers to engage with our stories and with each other: comments and social sharing buttons. You can comment at the bottom of any post, and above each post you’ll find convenient social media sharing buttons for Twitter, Reddit, Facebook, Google+, LinkedIn, and email. Please get involved in the conversation, and share it with your network.

Parallel Forall also now helps you find more content of interest. First is an improved search (see the search box in the sidebar). Second, at the bottom of each story you will see recommendations of related posts. And finally, we’re featuring our wonderful authors more prominently — each of their posts will feature their photo and bio, with a link to that author’s archive page with all of their posts on Parallel Forall.

For the past few months we have featured three regular post series, CUDACasts, CUDA Pro Tips, and CUDA Spotlights, so the new Parallel Forall menu bar highlights these series for quick access. CUDACasts are short, informative “screencast” videos that teach you about parallel programming techniques and CUDA features and strategies. CUDA Pro Tips provide concise, focused insight into useful techniques on the CUDA platform. CUDA Spotlights are in-depth interviews with people who are using CUDA successfully for solving difficult problems across a broad range of fields.

In addition to these series, we will continue to feature a variety of in-depth posts and series on parallel programming on the CUDA platform, such as our popular Introduction to CUDA C/C++ series, and our recent posts on GPU computing in MATLAB and Python.

We hope you agree that Parallel Forall is a great resource for programming on the CUDA platform, and we need your help to make it even better. So visit Parallel Forall regularly, subscribe to the feed (you can also subscribe via email using the button in the sidebar), share posts with your friends, join in the discussion, and let us know what you like and what you’d like to read about in the future.

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About Mark Harris

Mark is Chief Technologist for GPU Computing Software at NVIDIA. Mark has fifteen years of experience developing software for GPUs, ranging from graphics and games, to physically-based simulation, to parallel algorithms and high-performance computing. Mark has been using GPUs for general-purpose computing since before they even supported floating point arithmetic. While a Ph.D. student at UNC he recognized this nascent trend and coined a name for it: GPGPU (General-Purpose computing on Graphics Processing Units), and started GPGPU.org to provide a forum for those working in the field to share and discuss their work. Follow @harrism on Twitter
  • Manish Harsh

    Good going…