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Caracal: dynamic translation of runtime environments for GPUs

Rodrigo Dominguez, Dana Schaa, David Kaeli
Department of Electrical and Computer Engineering, Northeastern University, Boston, MA
GPGPU-4 Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units, 2011

@inproceedings{dominguez2011caracal,

   title={Caracal: Dynamic translation of runtime environments for gpus},

   author={Dominguez, R. and Schaa, D. and Kaeli, D.},

   booktitle={Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units},

   pages={5},

   year={2011},

   organization={ACM}

}

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Graphics Processing Units (GPU) have become the platform of choice for accelerating a large range of data parallel and task parallel applications. Both AMD and NVIDIA have developed GPU implementations targeted at the high performance computing market. The rapid adoption of GPU computing has been greatly aided by the introduction of high-level programming environments such as NVIDIA’s CUDA C and Khronos’ OpenCL. Given the fact that CUDA C has been on the market for a number of years, a large number of applications have been developed in the HPC community. In this paper we describe Caracal, our implementation of a dynamic translation framework that allows CUDA C programs to run on alternative GPU platforms. Here we target the AMD Evergreen family of GPUs. We discuss the challenges of compatibility and correctness faced by the translator using specific examples. We analyze the overhead of the translator compared with the execution time of several benchmarks. We also compare the quality of the code generated by our framework with that produced by the AMD OpenCL library. Our dynamically translated code performs comparably to the native OpenCL library, expands the opportunities for running CUDA C on new heterogeneous architectures, and provides a vehicle for evaluating compiler optimizations in the future.
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