IMPROVING PERFORMANCE OF DATA-CENTRIC SYSTEMS THROUGH FINE-GRAINED CODE GENERATION
thesisposted on 20.12.2019 by Gregory M Essertel
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
The availability of modern hardware with large amounts of memory created a shift in the development of data-centric software; from optimizing I/O operations to optimizing computation. As a result, the main challenge has become using the memory hierarchy (cache, RAM, distributed, etc) efficiently. In order to overcome this difficulty, programmers of data-centric programs need to use low-level APIs such as Pthreads or MPI to manually optimize their software because of the intrinsic difficulties and the low productivity of these APIs. Data-centric systems such as Apache Spark are becoming more and more popular. These kinds of systems offer a much simpler interface and allow programmers and scientists to write in a few lines what would have been thousands of lines of low-level MPI code. The core benefit of these systems comes from the introduction of deferred APIs; the code written by the programmer is actually building a graph representation of the computation that has to be executed. This graph can then be optimized and compiled to achieve higher performance.
In this dissertation, we analyze the limitations of current data-centric systems such as Apache Spark, on relational and heterogeneous workloads interacting with machine learning frameworks. We show that the compilation of queries in multiples stages and the interfacing with external systems is a key impediment to performance because of their inability to optimize across code boundaries. We present Flare, an accelerator for data-centric software, which provides performance comparable to the state of the art relational systems while keeping the expressiveness of high-level deferred APIs. Flare displays order of magnitude speed up on programs combining relational processing and machine learning frameworks such as TensorFlow. We look at the impact of compilation on short-running jobs and propose an on-stack-replacement mechanism for generative programming to decrease the overhead introduced by the compilation step. We show that this mechanism can also be used in a more generic way within source-to-source compilers. We develop a new kind of static analysis that allows the reverse engineering of legacy codes in order to optimize them with Flare. The novelty of the analysis is also useful for more generic problems such as formal verification of programs using dynamic allocation. We have implemented a prototype that successfully verifies programs within the SV-COMP benchmark suite.