An adaptive performance modeling tool for GPU architectures
Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
GPURoofline: a model for guiding performance optimizations on GPUs
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
An insightful program performance tuning chain for GPU computing
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
A memory access model for highly-threaded many-core architectures
Future Generation Computer Systems
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Using modern graphics processing units for no-graphics high performance computing is motivated by their enhanced programmability, attractive price/performance ratio and incredible growth in speed. Although the pipeline of a modern graphics processing unit (GPU) permits high throughput and more concurrency, they bring more complexities in analyzing the performance of GPU-based applications. In this paper, we identify factors that determine performance of GPU-based applications. We then classify them into three categories: data-linear, data-constant and computation-dependent. According to the characteristics of these factors, we propose a performance model for each factor. These models are then used to predict the performance of bio-sequence database scanning application on GPUs. Theoretical analyses and measurements show that our models can achieve precise performance predictions.