Analyzing Execution Time of Jit4gpu and Jit4opencl Against CPU Benchmarks

Abstract

There is a critical need to optimize execution time due to the increasing computing needs of current applications. By optimizing code dynamically during runtime, JIT compilation provides a dynamic method to code optimization and has the ability to improve speed for activities that need parallel processing. For very parallel workloads, JIT4GPU is the way to go, and JIT4OPENCL is all about making the most of the OpenCL framework so that parallel computation may happen on many platforms. To achieve great performance in jobs demanding heavy calculation, JIT4GPU optimizes code to fully use the parallel processing capabilities of GPUs, making it particularly designed for GPU architectures. But JIT4OpenCL is all about the OpenCL platform, which means it's more versatile and can run on a wide range of hardware, including GPUs and CPUs. The experimental assessment compares the execution time of CPU-optimized benchmarks to those built using jit4GPU and jit4OpenCL.