To improve the file storage efficiency of large datasets, big data analytics usually use some common file formats, such as Apache ORC (optimized row columnar) format, to encode and compress the data. However, this shifts the IO bottleneck (especially with high-bandwidth SSDs) to the computation bottleneck on CPUs to decompress and decode the data. This paper presents FORC, a high-throughput streaming-based FPGA accelerator overlay that supports different ORC file format decoders, and its dataflow integration with Apache ORC. Experimental results show that FORC achieves up to $12.9 \mathrm{~GB} / \mathrm{s}$ decoding throughput on AMD/Xilinx Alveo U280 FPGA, with a geomean speedup of 65x (up to 335x) over the CPU. FORC will be released soon at https://github.com/SFU-HiAccel/FORC.

Large teams of practitioners (developers, testers, etc.) usually work in parallel on the same code base. A major concern when working in parallel is the introduction of integration bugs in the latest shared code. These latent bugs are likely to slow down the project unless they are discovered as soon as possible. Many companies have adopted daily or weekly processes which build the latest source code and certify it by executing simple manual smoke/sanity tests or extensive automated integration test suites. Other members of a team can then use the certified build to develop new features or to perform additional analysis, such as performance or usability testing. For large projects the certification process may take a few days. This long certification process forces team members to either use outdated or uncertified (possibly buggy) versions of the code. In this paper, we create decision trees to predict ahead of time the certification result of a build. By accurately predicting the outcome of the certification process, members of large software teams can work more effectively in parallel. Members can start using the latest code without waiting for the certification process to be completed. To perform our study, we mine historical information (code changes and certification results) for a large software project which is being developed at the IBM Toronto Labs. Our study shows that using a combination of project attributes (such as the number of modified subsystems in a build and certification results of previous builds), we can correctly predict 69% of the time that a build will fail certification. We can as well correctly predict 95% of the time if a build will pass certification