Softshell

Steinberger, Markus; Kainz, Bernhard; Kerbl, Bernhard; Hauswiesner, Stefan; Kenzel, Michael; Schmalstieg, Dieter

doi:10.1145/2366145.2366180

Cited by 42 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Balancing Workload on GPUs. Workload balancing for GPUs is a classic problem studied for decades [8,16,19,49,50,53,58]. [56] exploits software-level control of scheduling on streaming multiprocessors.…”

Section: Related Workmentioning

confidence: 99%

AGAThA: Fast and Efficient GPU Acceleration of Guided Sequence Alignment for Long Read Mapping

Park,

Hong,

Song

et al. 2024

Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming

View full text Add to dashboard Cite

With the advance in genome sequencing technology, the lengths of deoxyribonucleic acid (DNA) sequencing results are rapidly increasing at lower prices than ever. However, the longer lengths come at the cost of a heavy computational burden on aligning them. For example, aligning sequences to a human reference genome can take tens or even hundreds of hours. The current de facto standard approach for alignment is based on the guided dynamic programming method. Although this takes a long time and could potentially benefit from high-throughput graphic processing units (GPUs), the existing GPU-accelerated approaches often compromise the algorithm's structure, due to the GPU-unfriendly nature of the computational pattern. Unfortunately, such compromise in the algorithm is not tolerable in the field, because sequence alignment is a part of complicated bioinformatics analysis pipelines. In such circumstances, we propose AGAThA, an exact and efficient GPU-based acceleration of guided sequence alignment. We diagnose and address the problems of the algorithm being unfriendly to GPUs, which comprises strided/redundant memory accesses and workload imbalances that are difficult to predict. According to the experiments on modern GPUs, AGAThA achieves 18.8× speedup against the CPU-based baseline, 9.6× against the best GPU-based baseline, and 3.6× against GPU-based algorithms with different heuristics.

show abstract

Section: Related Workmentioning

confidence: 99%

AGAThA: Fast and Efficient GPU Acceleration of Guided Sequence Alignment for Long Read Mapping

Park,

Hong,

Song

et al. 2024

Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming

View full text Add to dashboard Cite

show abstract

“…A variety of models have been proposed in the last years [6][5] [7], all of which are working as shown in Figure 1. In general, these schedulers work by creating a sufficient number of GPGPU threads and using some strategy to control the task generation and synchronization process.…”

Section: Related Workmentioning

confidence: 99%

“…Task scheduling on the GPU has been an important area of research in the last years [4][5] [6] [7], often with various specializations [1] [3]. These GPGPU task schedulers permit the creation of additional tasks from the GPU without CPU interference.…”

Section: Introductionmentioning

confidence: 99%

A GPU task generator for rendering

Petrescu

Moldoveanu

Asavei

et al. 2014

2014 18th International Conference on System Theory, Control and Computing (ICSTCC)

View full text Add to dashboard Cite

We present an original GPU task generator that can be attached to the rasterization based rendering process, in order to provide a dynamic parallelism. Compared to other existing GPU task generators or schedulers our method creates new tasks using the GPU graphics pipeline task scheduler. These are generated in the geometry shader, by means of rasterizing new geometry that produces additional fragments. The parallel processing of these fragments represents the new threads, which are used as general purpose threads. Thus, our solution is extremely lightweight and it can run in parallel with the rasterization based rendering process. By using our generator, work-heavy rendering threads can distribute their load to a sufficiently large group of workers without any CPU interference. By doing this we are effectively reducing the complexity on a per-rendering thread basis and therefore we are able to obtain a performance increase of 1 or even 2 orders of magnitude in critical cases. Tasks such as tracing rays, dynamic geometry generation, expensive BSSRDFs evaluation or ray marching are handled in a unified manner, making it easy to integrate our generator into game engines. In contrast with other GPU task generators and schedulers our method does not occupy all hardware threads nor does it need specialized hardware, only requiring Shader Model 5. Since rarely are dynamic rendering tasks complicated enough to generate dynamic child tasks on their own, our generator can be configured to run as just a simple non recursive task dispatcher.Keywords-rasterization based rendering, task scheduling on GPU, dynamic parallelism, dynamic task dispatch.

show abstract

“…Ao desenvolver a implementação na GPU, visou-se fazê-lo na arquitetura Kepler. [30]. No entanto, essa decisão impõe a pré-alocação da memória em quantidade suficiente para que se comportem todos os itens de trabalho, inclusive os adicionados dinamicamente durante a execução.…”

Section: Figura 318 Fluxograma Das Partes Do Processo De Geração Na Gpuunclassified

Geração De Malhas Rodoviárias Na Gpu

GERMANO¹

View full text Add to dashboard Cite

Softshell

Abstract: 24.10.14 KV. Ok to add accepted version to spira

Cited by 42 publications

References 22 publications

AGAThA: Fast and Efficient GPU Acceleration of Guided Sequence Alignment for Long Read Mapping

AGAThA: Fast and Efficient GPU Acceleration of Guided Sequence Alignment for Long Read Mapping

A GPU task generator for rendering

Geração De Malhas Rodoviárias Na Gpu

Contact Info

Product

Resources

About