Sparse matrix-vector multiplications are widely used for many scientific computations, such as graph algorithms [1], graphics processing [2, 3], numerical analysis [10], and conjugate gradients [14]. This proble
The matrix represents a finite-difference approximation to the Laplacian operator on a 5-by-5 mesh. of sparse matrix-vector multiplication, we are not concerned with modifying matrices, we will only consider static sparse matrix formats, as opposed to those suitable for rapid insertion and ...
Sparse Matrix-Vector multiplication (SpMV) is one of the key operations in linear algebra. Overcoming thread divergence, load imbalance and un-coalesced and indirect memory access due to sparsity and irregularity are challenges to optimizing SpMV on GPUs. This dissertation develops solutions that ...
Sparse matrix-vector multiplication (SpMV) is one of the important subroutines in numerical linear algebras widely used in lots of large-scale applications. Accelerating SpMV on multicore and manycore architectures based on Compressed Sparse Row (CSR) format via row-wise parallelization is one of th...
系统标签: sparse multiplication matrix vector using unary SparseMatrixSparseVectorMultiplicationusingParallelandReconfigurableComputingAThesisPresentedfortheMasterofScienceDegreeTheUniversityofTennesseeKirkAndrewBaugherMay2004DedicationThisthesisisdedicatedtomylovingwifeandourfamiliesfortheirmotivationandsupport,whichhasinspiredme...
[kernel version (1, 2, or 3)]: 1: the regular sparse matrix-vector multiplication in Nvidia's cuSparse; 2: the optimized sparse matrix-vector multiplication in Nvidia's cuSparse; 3: the sparse matrix-vector multiplication implemented in CSR5. Kernel version will be applied to SpVM version ...
scala sparseVetor, SprseMatrix 实现 def rand(seed:Int):Double={ val rand=new Random(seed) rand.nextDouble() } def rand2(size:Int,seed:Int):SparseVector={ val rand=new Random(seed) val ret_indices=Array.range(0,size) val ret_values=Array.tabulate[Double](size)(i=>rand.nextDouble()...
ChipSum主框架的实现方案为高阶过程,数据(如向量的数据、稀疏矩阵的邻接表等)采用抽象类型特性实现,过程则采用重载和可变参数模板等特性实现。这里我未来将抽时间解释一下具体实现机制。 // TODOMember gassive commented Aug 16, 2021 merge前把所有的issue都close掉 gassive closed this as completed Aug 30, ...
在上述背景下,2018年,一种称为稀疏矢量码(Sparse Vector Coding,SVC)的短包传输技术被提出[1]。与传统传输方式不同的是,SVC将发送的信息比特映射到一个稀疏向量的少量非零索引上,并通过一个非正交码本将该稀疏向量随机扩展到一个低维的序列,最后将该序列映射到时频资源进行传输。在接收端,接收机只需检测接收信...
An example of matrix–vector multiplication and accumulation. A sequential implementation of SpMV based on the CSR format is quite straightforward, as shown in Fig. 10.4. We assume that the code has access to (1) num_rows, a function argument that specifies the number of rows in the sparse...