“…There are two main features for applications offloading. The first one is the loop statements suitable for a GPU are extracted using evolutionary computation [21] of the genetic algorithm (GA) [28], and the variables used in nested loop statements are transferred between the CPU and GPU in the outer-most loop possible [20].…”
Section: Previous Automatic Gpu Offloading Methods and Its Problemsmentioning
confidence: 99%
“…In addition to the appropriate extraction of loop statements, I previously proposed to transfer as many variables used in the nested loop statements as possible to the upper level loop to reduce the number of transfers between CPU and GPU [20]. When offloading a loop statement to a GPU with a naive method, if CPU-GPU transfer is executed at the lower level of nesting, transfer is done at each lower-level loop, which is not efficient.…”
Section: Previous Automatic Gpu Offloading Methods and Its Problemsmentioning
confidence: 99%
“…In my previous studies, I attempted to automate the trial and error of parallel processing areas [20,21]. Loop statements suitable for GPU offloading were extracted appropriately by repeating the performance measurements in the verification environment using evolutionary computation, and as many variables in the nested loop statements as possible were transferred in the upper loop.…”
Section: Current Heterogeneous Hardware Technologiesmentioning
confidence: 99%
“…I previously proposed environment adaptive software that effectively runs once-written applications by automatically executing code conversion and configurations so that GPUs, FPGAs, and IoT devices can be effectively used on deployment environments [20]. Even if the performance is not so high compared to manual tuning of highly skilled engineers, automation is important, I think.…”
Section: Introductionmentioning
confidence: 99%
“…As part of environment adaptive software, I also proposed a method to offload loop statements of applications to GPUs [21] and FPGAs [22] automatically. In this paper, I improved upon the GPU offloading method, which I previously proposed using evolutionary computation [20,21], to be applied to more applications with higher performance. I implemented this method and evaluated its effectiveness for several applications.…”
With the slowing down of Moore's law, the use of hardware other than CPUs, such as graphics processing units (GPUs) or field-Programmable gate arrays (FPGAs), is increasing. However, when using heterogeneous hardware other than CPUs, barriers to technical skills, such for compute unified device architecture (CUDA) and open computing language (OpenCL), are high. Therefore, I previously proposed environment adaptive software that enables automatic conversion, configuration, and high-performance operation of once written code according to the hardware to be placed. As part of environment adaptive software, I also proposed a method to offload loop statements of applications to GPUs automatically. In this paper, I improved upon this automatic GPU offloading method to expand its applicability to more applications and enhance offloading performance. I implemented the improved method to evaluate its effectiveness for multiple applications.
“…There are two main features for applications offloading. The first one is the loop statements suitable for a GPU are extracted using evolutionary computation [21] of the genetic algorithm (GA) [28], and the variables used in nested loop statements are transferred between the CPU and GPU in the outer-most loop possible [20].…”
Section: Previous Automatic Gpu Offloading Methods and Its Problemsmentioning
confidence: 99%
“…In addition to the appropriate extraction of loop statements, I previously proposed to transfer as many variables used in the nested loop statements as possible to the upper level loop to reduce the number of transfers between CPU and GPU [20]. When offloading a loop statement to a GPU with a naive method, if CPU-GPU transfer is executed at the lower level of nesting, transfer is done at each lower-level loop, which is not efficient.…”
Section: Previous Automatic Gpu Offloading Methods and Its Problemsmentioning
confidence: 99%
“…In my previous studies, I attempted to automate the trial and error of parallel processing areas [20,21]. Loop statements suitable for GPU offloading were extracted appropriately by repeating the performance measurements in the verification environment using evolutionary computation, and as many variables in the nested loop statements as possible were transferred in the upper loop.…”
Section: Current Heterogeneous Hardware Technologiesmentioning
confidence: 99%
“…I previously proposed environment adaptive software that effectively runs once-written applications by automatically executing code conversion and configurations so that GPUs, FPGAs, and IoT devices can be effectively used on deployment environments [20]. Even if the performance is not so high compared to manual tuning of highly skilled engineers, automation is important, I think.…”
Section: Introductionmentioning
confidence: 99%
“…As part of environment adaptive software, I also proposed a method to offload loop statements of applications to GPUs [21] and FPGAs [22] automatically. In this paper, I improved upon the GPU offloading method, which I previously proposed using evolutionary computation [20,21], to be applied to more applications with higher performance. I implemented this method and evaluated its effectiveness for several applications.…”
With the slowing down of Moore's law, the use of hardware other than CPUs, such as graphics processing units (GPUs) or field-Programmable gate arrays (FPGAs), is increasing. However, when using heterogeneous hardware other than CPUs, barriers to technical skills, such for compute unified device architecture (CUDA) and open computing language (OpenCL), are high. Therefore, I previously proposed environment adaptive software that enables automatic conversion, configuration, and high-performance operation of once written code according to the hardware to be placed. As part of environment adaptive software, I also proposed a method to offload loop statements of applications to GPUs automatically. In this paper, I improved upon this automatic GPU offloading method to expand its applicability to more applications and enhance offloading performance. I implemented the improved method to evaluate its effectiveness for multiple applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.