Statistical Analysis of Parallel Data Uploading using OpenGL

Abstract

Modern real-time visualizations of large-scale datasets require constant high frame rates while their datasets might exceed the available graphics memory. This requires sophisticated upload strategies from host memory to the memory of the graphics cards. A possible solution uses outsourcing of all data uploads onto concurrent threads and disconnecting prohibitive data dependencies. OpenGL provides a variety of functions and parameters but not all allow minimal interference on rendering. In this work, we present a thorough and statistically sound analysis of various effects introduced by choosing different input parameters, such as size, partitioning and number of threads for uploading, as well as combinations of buffer usage hints and uploading functions. This approach provides insight into the problem and offers a basis for future optimizations.