Abstract. Moore’s Law suggests that the number of processing cores on a single chip increases exponentially. The future performance in-creases will be mainly extracted from thread-level parallelism exploited by multi/many-core processors (MCP). Therefore, it is necessary to find out how to build the MCP hardware and how to program the paral-lelism on such MCP. In this work, we intend to identity the key archi-tecture mechanisms and software optimizations to guarantee high per-formance for multithreaded programs. To illustrate this, we customize a dense matrix multiplication algorithm on Godson-T MCP as a case study to demonstrate the efficient synergy and interaction between hard-ware and software. Experiments conducted on the cycle-accurat...
For the past decade, power/energy consumption has become a limiting factor for large-scale and embed...
In this project I optimized the Dense Matrix-Matrix multiplication calculation by tiling the matrice...
Abstract: Few realize that, for large matrices, many dense matrix computations achieve nearly the sa...
Abstract. Traditional parallel programming methodologies for improv-ing performance assume cache-bas...
Out-of-core implementations of algorithms for dense matrix computations have traditionally focused o...
AbstractIn this article, we present a fast algorithm for matrix multiplication optimized for recent ...
While the growing number of cores per chip allows researchers to solve larger scientific and enginee...
This Master Thesis examines if a matrix multiplication program that combines the two efficiency stra...
Many-Task Computing (MTC) is a common scenario for multiple parallel systems, such as cluster, grids...
Until recently, performance gains in processors were achieved largely by improvements in clock speed...
A number of parallel formulations of dense matrix multiplication algorithm have been developed. For ...
This paper talks about different types of algorithms fro matrix multiplication when applied to paral...
Abstract. This paper presents a study of performance optimization of dense matrix multiplication on ...
As users and developers, we are witnessing the opening of a new computing scenario: the introduction...
Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely used in are...
For the past decade, power/energy consumption has become a limiting factor for large-scale and embed...
In this project I optimized the Dense Matrix-Matrix multiplication calculation by tiling the matrice...
Abstract: Few realize that, for large matrices, many dense matrix computations achieve nearly the sa...
Abstract. Traditional parallel programming methodologies for improv-ing performance assume cache-bas...
Out-of-core implementations of algorithms for dense matrix computations have traditionally focused o...
AbstractIn this article, we present a fast algorithm for matrix multiplication optimized for recent ...
While the growing number of cores per chip allows researchers to solve larger scientific and enginee...
This Master Thesis examines if a matrix multiplication program that combines the two efficiency stra...
Many-Task Computing (MTC) is a common scenario for multiple parallel systems, such as cluster, grids...
Until recently, performance gains in processors were achieved largely by improvements in clock speed...
A number of parallel formulations of dense matrix multiplication algorithm have been developed. For ...
This paper talks about different types of algorithms fro matrix multiplication when applied to paral...
Abstract. This paper presents a study of performance optimization of dense matrix multiplication on ...
As users and developers, we are witnessing the opening of a new computing scenario: the introduction...
Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely used in are...
For the past decade, power/energy consumption has become a limiting factor for large-scale and embed...
In this project I optimized the Dense Matrix-Matrix multiplication calculation by tiling the matrice...
Abstract: Few realize that, for large matrices, many dense matrix computations achieve nearly the sa...