• Sparse Systems Solving on {GPUs} with {GMRES}🔍
• Unsupervised Feature Learning Via Sparse Hierarchical ...🔍
• The Best GPUs for Deep Learning in 2023🔍
• Expressing High Performance Irregular Computations on the GPU🔍
• Efficient sparse coding algorithms🔍
• [D] What happened to sparse tensors ? 🔍
• Accelerating Matrix|Centric Graph Processing on GPU through Bit ...🔍
• Harnessing GPU Tensor Cores for Fast FP16 Arithmetic to Speed up ...🔍

(PDF) Fast GPU implementation of large scale dictionary and sparse ...

Sparse Kernel-Single Value Decomposition algorithm is applied for optimization to fasten the sparse coding process. Few experiments with real images depict that ...

Sparse Systems Solving on {GPUs} with {GMRES} - ResearchGate

describes the NVIDIA GPUs architecture and its coding principles with CUDA [12]. Section 3presents the GMRES algorithm and the requirements to adapt it for GPUs ...

Unsupervised Feature Learning Via Sparse Hierarchical ...

We first apply the exponential family sparse coding algorithms to two self ... Our algorithms result in a significant speedup for sparse coding, allowing us.

TileSpGEMM: A Tiled Algorithm for Parallel Sparse General Matrix ...

than sparse multiplication used in the TileSpGEMM. mc2depi ... Optimizing sparse matrix operations on gpus using merge path. In ...

The Best GPUs for Deep Learning in 2023 - Tim Dettmers

Figure 2: The sparse matrix is compressed to a dense representation before the matrix multiplication is performed. The figure is taken from Jeff ...

Expressing High Performance Irregular Computations on the GPU

... in sparse machine learning and linear algebra ... The result is complex code whose load-balance capabilities cannot easily be used in other ...

Efficient sparse coding algorithms - NIPS papers

Our algorithms result in a significant speedup for sparse coding, ... In related work [8], we apply this to self-taught learning, a new machine learning formalism ...

SparseProp: Efficient Sparse Backpropagation for Faster Training of ...

Our algorithm, called SparseProp, is general in the sense that. 1) it applies to arbitrary sparsity patterns, 2) general layer types, and 3) can be ...

[D] What happened to sparse tensors ? : r/MachineLearning - Reddit

So if you have 2 matrices with 50% sparsity, the sparse matrix multiplication algorithm is much slower than a dense algorithm due to the ...

Accelerating Matrix-Centric Graph Processing on GPU through Bit ...

Table 5: BMV schemes used in the algorithms. ... Demmel, Reduced-bandwidth multi- threaded algorithms for sparse matrix-vector multiplication, in: 2011 IEEE.

Harnessing GPU Tensor Cores for Fast FP16 Arithmetic to Speed up ...

factorization algorithms; and here we mean that the fac- torization itself is implemented in multi-precision despite the fact the iterative refinement is mixed ...

Block-wise dynamic mixed-precision for sparse matrix-vector ...

The index compression method aims to reduce the size of indices in sparse matrices. It utilizes encoding techniques to compress row and column ...

A deep dive into faster semantic sparse retrieval in OpenSearch 2.12

Model inference is a throughput bottleneck for neural sparse ingestion and bi-encoder search. Using a GPU can significantly accelerate model ...

Support Regularized Sparse Coding and Its Fast Encoder

We present the optimization algorithm of SRSC with theoretical guarantee for the optimization over the sparse codes. We also propose a feed ...

Combining approximate inference methods for efficient learning on ...

2 Parallel EM Learning in Sparse Coding Models. We discuss parallelization of our algorithms for training Sparse Coding (SC) models on large data sets.

Fast GPU-based Collision Detection for Deformable Models - GAMMA

Stream compaction is often used on GPUs to remove unwanted el- ements in a sparse data representation. ... modules used in the algorithm (i.e. updating BVHs, ...

Massive parallelization of serial inference algorithms for a complex ...

Instead of uploading 2 × N floating-point numbers to the GPU in each cycle step, we succeed in reducing this number to a single floating-point Δβj. The cost, of ...

torch.sparse — PyTorch 2.5 documentation

By compressing repeat zeros sparse storage formats aim to save memory and computational resources on various CPUs and GPUs. Especially for high degrees of ...

How should I verify the speedup effect of the algorithm? #15 - GitHub

Hi, SparseGPT itself is just concerned with accurately sparsifying a ... through speedup (such as 2:4 sparsity on Ampere GPUs). Our ...

A GPU-based algorithm for fast node label learning in large and ...

The key strategy to speed up the computations is to partition nodes into independent sets so as to process each set in parallel by exploiting ...