This paper introduces Tiramisu, a polyhedral framework designed to generate high
performance code for multiple platforms including multicores, GPUs, and distributed …

The increasing importance of graph-data based applications is fueling the need for highly
efficient and parallel implementations of graph analysis software. In this paper we describe …

Develo** high-performance software is a difficult task that requires the use of low-level,
architecture-specific programming models (eg, OpenMP for CMPs, CUDA for GPUs, MPI for …

As the size of datasets continues to grow, machine learning applications are becoming
increasingly limited by the amount of available computational power. Taking advantage of …

Computers have become increasingly complex with the emergence of heterogeneous
hardware combining multicore CPUs and GPUs. These parallel systems exhibit tremendous …

Computing systems are becoming increasingly parallel and heterogeneous, and therefore
new applications must be capable of exploiting parallelism in order to continue achieving …

Programming accelerators such as GPUs with low-level APIs and languages such as
OpenCL and CUDA is difficult, error-prone, and not performance-portable. Automatic …

Domain-specific languages (DSLs) are able to provide intuitive high-level abstractions that
are easy to work with while attaining better performance than general-purpose languages …

As the computational power of GPUs continues to scale with Moore's Law, an increasing
number of applications are becoming limited by memory bandwidth. We propose an …

We perform the first study of the tradeoff space of access methods and replication to support
statistical analytics using first-order methods executed in the main memory of a Non-Uniform …