COLUMN: Letter from the editors
By CACM Staff
Solving large-scale, complex problems, such as climate change or nuclear stockpile stewardship, would be next to impossible without scientific computing. And thanks to advances in high-performance computing, scientific computing continues to flourish. Scientific computing, also known as computational science, emphasizes interdisciplinary collaboration in the development of computer programs, software applications, and computer simulations. The intersection of computer science, engineering, and applied mathematics is at the heart of scientific computing; computer-based models are used to analyze diverse scientific problems across biology, geology, chemistry, ecology, climatology, and physics, to name a few. For those of you who are computational scientists, or leaning in that direction, this issue provides a comprehensive overview of a diverse and growing field.
By CACM Staff
By Nick Knight, Jack Poulson
By Santosh Kalwar
By Daniel Gooch
By Ben Deverett
The XRDS blog highlights a range of topics from conference overviews to privacy and security, from HCI to cryptography. Selected blog posts, edited for print, will be featured in every issue. Please visit xrds.acm.org/blog to read each post in its entirety.
By Lea Rosen
By Dimitris Mitropoulos
Climate modeling has come a long way since von Neumann declared it a problem too hard for pencil and paper, but tailor-made for the new digital computers. As the models and computers both evolve toward ever-greater complexity, they are changing our notions of digital simulation itself.
By V. Balaji
A survey of radiation modeling and circuit simulation approaches that are essential for stockpile stewardship.
By Heidi K. Thornquist, Eric R. Keiter, Sivasankaran Rajamanickam
On the computational resources and techniques required for imaging the Earth's crust.
By Gregory A. Newman
Interesting problems in computational chemistry from a computer science perspective.
By Jeff R. Hammond
Scientific computing for social and modern information networks.
By David Gleich
Analyzing massive streaming graphs efficiently requires new algorithms, data structures, and computing platforms.
By Jason Riedy, David A. Bader
Recent advances in natural language processing bring together rich representations and scalable machine learning algorithms.
By Noah A. Smith, André F. T. Martins
Do we need to design algorithms differently if our goal is to save energy, rather than time or space? This article presents a simple and speculative thought experiment that suggests when and why the answer could be "yes."
By Jee Whan Choi, Richard W. Vuduc
Infrastructure clouds offer tremendous potential for scientific users, however, they face numerous challenges that must be addressed before they are widely adopted by scientific communities.
By Paul Marshall, Henry Tufo, Kate Keahey
A new system allows researchers to discover, reuse, cite, and experiment upon any computational result that is published with a Verifiable Result Identifier.
By Matan Gavish, David Donoho, Amos Onn
By Adrian Scoică, Arthur S. Bland
By Anshul Vikram Pandey
By Finn Kuusisto
By Marinka Zitnik