This work is a continuation and augmentation of previous energy studies ofCompressed Sparse eXtended (CSX), a framework for efficiently executing SparseMatrix-Vector Multiplication (SpMV).CSX was developed by the CSLab at the National Technical University of Athens(NTUA), and utilizes compression to overcome a significant memory bottleneckinherent in SpMV, thus increasing performance and energy efficiency of itsexecution.SpMV is notorious within scientific computing for its low performance. However,the problem is unavoidable, as SpMV can be found within several scientificapplications. In this work, CSX is tested as the SpMV kernel in a frameworkimplementing the Conjugate Gradient Method (CG), an iterative algorithm forsolving specific linea...
General purpose computation on graphics processing unit (GPU) is prominent in the high performance c...
Abstract. A new format for storing sparse matrices is proposed for efficient sparse matrix-vector (S...
It is well-known that reordering techniques applied to sparse matrices are common strategies to impr...
This work is a continuation and augmentation of previous energy studies ofCompressed Sparse eXtended...
Abstract—Sparse matrix-vector multiplication (SpM×V) has been characterized as one of the most signi...
The Sparse Matrix-Vector multiplication (SpMV) kernel scales poorly on shared memory systems with mu...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
Multiphysics simulations are at the core of modern Computer Aided Engineering (CAE) allowing the ana...
Multiphysics simulations are at the core of modern Computer Aided Engineering (CAE) allowing the ana...
Sparse matrix-vector multiplication (shortly SpMV) is one of most common subroutines in the numerica...
Due to ill performance on many devices, sparse matrix-vector multiplication (SpMV) normally requires...
Multiphysics simulations are at the core of modern Computer Aided Engineering (CAE) allowing the ana...
Graphics processing units (GPUs) have delivered a remarkable performance for a variety of high perfo...
General purpose computation on graphics processing unit (GPU) is prominent in the high performance c...
In this dissertation we have identified vector processing shortcomings related to the efficient stor...
General purpose computation on graphics processing unit (GPU) is prominent in the high performance c...
Abstract. A new format for storing sparse matrices is proposed for efficient sparse matrix-vector (S...
It is well-known that reordering techniques applied to sparse matrices are common strategies to impr...
This work is a continuation and augmentation of previous energy studies ofCompressed Sparse eXtended...
Abstract—Sparse matrix-vector multiplication (SpM×V) has been characterized as one of the most signi...
The Sparse Matrix-Vector multiplication (SpMV) kernel scales poorly on shared memory systems with mu...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
Multiphysics simulations are at the core of modern Computer Aided Engineering (CAE) allowing the ana...
Multiphysics simulations are at the core of modern Computer Aided Engineering (CAE) allowing the ana...
Sparse matrix-vector multiplication (shortly SpMV) is one of most common subroutines in the numerica...
Due to ill performance on many devices, sparse matrix-vector multiplication (SpMV) normally requires...
Multiphysics simulations are at the core of modern Computer Aided Engineering (CAE) allowing the ana...
Graphics processing units (GPUs) have delivered a remarkable performance for a variety of high perfo...
General purpose computation on graphics processing unit (GPU) is prominent in the high performance c...
In this dissertation we have identified vector processing shortcomings related to the efficient stor...
General purpose computation on graphics processing unit (GPU) is prominent in the high performance c...
Abstract. A new format for storing sparse matrices is proposed for efficient sparse matrix-vector (S...
It is well-known that reordering techniques applied to sparse matrices are common strategies to impr...