This paper systematically evaluates the performance across three different native vectorized execution engines, including C++ based Velox and ClickHouse, and Rust based DataFusion, for the widely used big data engine Apache Spark, and proposes a cost model driven operator placement strategy that adaptively schedules operators between the native vectorized engine and the JVM code generation engine.
LMTracer is presented, a fine-grained and real-time performance profiling framework for production LLM services that embed profiling logic into the execution through graph-embedded probing and streaming buffered profiling data to CPUs on demand to keep the execution of user kernels uninterrupted.
Wei Liu, Yong-Chao He, Bo-Han Zhao et al.· Proceedings of the ACM SIGOP...· 0 citations
A benchmarking study of Python-Rust interoperability reveals that the Rust enhanced versions systematically execute faster than pure Pythoncode- up to three times faster in some cases.
S. Singh, R.pradeep Raj· International Journal For Mu...· 0 citations
The approach provided here unifies the profiling, benchmark based analysis, bottleneck detection, focused optimization, and quantitative validation into a single procedure and provides practical recommendations to improve computational performance, scalability, and resource consumption of data-intensive Python systems...
Madhurima Kommuru, Appala Nooka Kumar Doodala· International Journal of App...· 0 citations
Torchy is presented, a tracing JIT compiler for PyTorch, one of the mainstream eager-mode frameworks, that achieves similar performance as data-flow frameworks, while providing the same semantics of straight-away execution.
A low-latency MLIR compilation backend based on TPDE that by-passes the LLVM lowering and compilation pipeline is introduced and techniques that reduce MLIR’s intrinsic overhead are proposed.
Jonas Ladner, Lukas Döllerer, Alexis Engelke et al.· 0 citations
This thesis presents a performance evaluation of three widely used open-source inference engines: vLLM, SGLang, and llama.cpp, and summarizes the experimental findings into selection recommendations for practical deployment scenarios, providing a reference for developers and researchers in choosing an appropriate infer...
Unknown authors· 0 citations
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