We investigate the technologies that support scalable high-performance computing, including hardware, software, and theory.
Our goal is to make scalable computing simpler, faster, and more effective. The group is currently engaged in developing tools to simplify parallel computing, such as the Cilk multithreaded programming platform and its related tools for programmer productivity. In addition, we are developing parallel applications, focusing in particular on irregular problems such as graph applications. The group also studies adaptive computing, race detection, and cache-oblivious algorithms.
Wikipedia is one of the most widely accessed encyclopedia sites in the world, including by scientists. Our project aims to investigate just how far Wikipedia’s influence goes in shaping science.
Our goal is to develop lightweight tools that allow programmers to better understand the cache performance of their applications. Tasks include designing profilers, performance engineering existing ones, and exploring different metrics for cache interactions.
MIT CSAIL researchers have developed the first-ever AI Risk Repository: a comprehensive and accessible living database of 700+ risks posed by AI that will be continuously updated to ensure relevancy and timeliness.
A new study critically examines the economic practicality of using AI for automating tasks in the workplace, with a specific emphasis on computer vision.
The Advanced Computing Users Survey, sampling sentiments from 120 top-tier universities, national labs, federal agencies, and private firms, finds the decline in America’s advanced computing lead spans many areas.
Good Ventures awarded MIT researcher Neil Thompson a $13 million dollar grant, which he will use to fund FutureTech, an interdisciplinary effort to analyze how progress in computing creates economic growth.
Rapid increases in the speed and power of microchips have fueled innovation in many industries, but the future trajectory of that incredible progress may be in jeopardy.
