Performance portability is a major challenge faced today by developers on the heterogeneous high performance computers. This project consisted in the development of a tool for translating CUDA programs in C++. Our approach consists in writing the best possible code using the best possible frameworks and run it on the best possible hardware. This means that when we write our code, we don't think in advance about the legacy hardware on which our code will run, thus we can work with modern frameworks like CUDA. Another fundamental aspect is that we want to discover if CUDA can be an effective data-parallel programming model for more than just GPU architectures
Abstract. CUDA is a data parallel programming model that supports several key abstractions- thread b...
This chapter demonstrates how to leverage the Thrust parallel template library to implement high-per...
GPU-accelerated computing drives current scientific research. Writing fast numeric algorithms for GP...
CUDA programming language perfectly matches the data parallel programming model and it is a very spe...
AbstractGraphics processor units (GPUs) have evolved to handle throughput oriented workloads where a...
Parallel computing is a rapidly growing field due to its extreme performance boosts when dealing wit...
The proliferation of heterogeneous computing systems has led to increased interest in parallel archi...
AbstractThis paper proposes APTCC, Auto Parallelizing Translator from C to CUDA, a translator from C...
Graphics Processing Units (GPUs) have become a competitive accelerator for non-graphics application...
Parallel processing using GPUs provides substantial increases in algorithm performance across many d...
As an open, royalty-free framework for writing programs that execute across heterogeneous platforms,...
The use of modern, high-performance graphical processing units (GPUs) for acceleration of scientific...
Original article can be found at : http://portal.acm.org/ Copyright ACM [Full text of this article i...
The proliferation of accelerators, in particular GPUs, over the past decade is im- pacting the way s...
We present the results of a diploma thesis adding CUDA (runtime) C++ support to cling. Today's HPC s...
Abstract. CUDA is a data parallel programming model that supports several key abstractions- thread b...
This chapter demonstrates how to leverage the Thrust parallel template library to implement high-per...
GPU-accelerated computing drives current scientific research. Writing fast numeric algorithms for GP...
CUDA programming language perfectly matches the data parallel programming model and it is a very spe...
AbstractGraphics processor units (GPUs) have evolved to handle throughput oriented workloads where a...
Parallel computing is a rapidly growing field due to its extreme performance boosts when dealing wit...
The proliferation of heterogeneous computing systems has led to increased interest in parallel archi...
AbstractThis paper proposes APTCC, Auto Parallelizing Translator from C to CUDA, a translator from C...
Graphics Processing Units (GPUs) have become a competitive accelerator for non-graphics application...
Parallel processing using GPUs provides substantial increases in algorithm performance across many d...
As an open, royalty-free framework for writing programs that execute across heterogeneous platforms,...
The use of modern, high-performance graphical processing units (GPUs) for acceleration of scientific...
Original article can be found at : http://portal.acm.org/ Copyright ACM [Full text of this article i...
The proliferation of accelerators, in particular GPUs, over the past decade is im- pacting the way s...
We present the results of a diploma thesis adding CUDA (runtime) C++ support to cling. Today's HPC s...
Abstract. CUDA is a data parallel programming model that supports several key abstractions- thread b...
This chapter demonstrates how to leverage the Thrust parallel template library to implement high-per...
GPU-accelerated computing drives current scientific research. Writing fast numeric algorithms for GP...