Aug 13 2012 – 8:00am – Aug 17 2012 – 5:00pm
By studying many current GPU computing applications, we have learned that the limits of an application’s scalability are often related to some combination of memory bandwidth saturation, memory contention, imbalanced data distribution, or data structure/algorithm interactions. Successful GPU application developers often adjust their data structures and problem formulation specifically for massive threading and executed their threads leveraging shared on-chip memory resources for bigger impact. We looked for patterns among those transformations, and here present the seven most common and crucial algorithm and data optimization techniques we discovered.