The OpenCL is open standard allowing to program many types of accelerators as well as classic CPUs. It allows implementing highly parallel computational kernels executed on computing devices. The development and optimization of kernels is challenging even for experienced programmers, as it requires to efficiently parallelize the code to thousands of independent thread and follow many performance characteristics of current hardware (which changes significantly for different hardware types and even generations). Thus, any automatic tool, which eases the exploration of performance-related parameters, has a great value in this area.
We develop methods and tool KTT for autotuning the code, where the programmer identifies parameters of the code, which may have an influence on the performance, and the autotuning tool automatically searches the parameter space and pick ones which lead to the highest performance on a particular hardware device. The autotuning decreases the time needed for manual exploration of code tuning parameters and allows developers to write flexible codes, which optimize themselves for underlying hardware architecture automatically. We have extended the state-of-the-art autotuning methods to allow autotuning global optimizations (affecting multiple kernels and host code) and online autotuning (non-blocking tuning in during computation is performed). Currently, we are working towards improving tuning space search methods for online tuning.
Results (more papers are under review or prepared):
This work was supported by the project OP RD&E CERIT Scientific Cloud CZ.02.1.01/0.0/0.0/16_013/0001802
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