DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication
Sparse matrix-vector multiplication (SpMV) plays a key role in computational science and
engineering, graph processing, and machine learning applications. Much work on SpMV …
engineering, graph processing, and machine learning applications. Much work on SpMV …
Toward accelerated stencil computation by adapting tensor core unit on gpu
The Tensor Core Unit (TCU) has been increasingly adopted on modern high performance
processors, specialized in boosting the performance of general matrix multiplication …
processors, specialized in boosting the performance of general matrix multiplication …
Accelerating range minimum queries with ray tracing cores
Over the past decade, GPU technology has undergone a notable transformation, evolving
from pure general-purpose computation to the integration of application-specific integrated …
from pure general-purpose computation to the integration of application-specific integrated …
Modeling GPU Dynamic Parallelism for self similar density workloads
Dynamic Parallelism (DP) is a GPU programming abstraction that can make parallel
computation more efficient for problems that exhibit heterogeneous workloads. With DP …
computation more efficient for problems that exhibit heterogeneous workloads. With DP …
Bitmap-Based Sparse Matrix-Vector Multiplication with Tensor Cores
YA Chen, JX Yu - Proceedings of the 53rd International Conference on …, 2024 - dl.acm.org
Sparse matrix-vector multiplication (SpMV) plays a crucial role in various scientific and
engineering tasks. Thus, extensive research efforts are devoted to enhancing its …
engineering tasks. Thus, extensive research efforts are devoted to enhancing its …
A scalable and energy efficient GPU thread map for m-simplex domains
This work proposes a new GPU thread map for m-simplex domains that improves its
speedup along with the m-dimension and is energy efficient compared to other state of the …
speedup along with the m-dimension and is energy efficient compared to other state of the …
TensorCV: Accelerating Inference-Adjacent Computation Using Tensor Processors
The advancements in AI/ML accelerators have made the core AI/ML computation relatively
insignificant in application pipelines. For example, inferencing only accounts for 3% of the …
insignificant in application pipelines. For example, inferencing only accounts for 3% of the …