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Research with ocelot

Research with ocelot. Workload Characterization and Analysis. SM Load Imbalance (Mandelbrot). Intra-Thread Data Sharing. Activity Factor. Constructing Performance Models: Eiger. Develop a portable methodology to discover relationships between architectures and applications.

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Research with ocelot

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  1. Research with ocelot

  2. Workload Characterization and Analysis SM Load Imbalance (Mandelbrot) Intra-Thread Data Sharing Activity Factor

  3. Constructing Performance Models: Eiger • Develop a portable methodology to discover relationships between architectures and applications Adapteva’s multicore from electronicdesign.com • Extensions to Ocelotfor the synthesis of performance models • Used in macroscale simulation models • Used in JIT compilers to make optimization decisions • Used in run-times to make scheduling decisions

  4. Eiger Methodology • Use data analysis techniques to uncover application-architecture relationships • Discover and synthesize analytic models • Extensible in source data, analysis passes, model construction techniques, and destination/use Ocelot JIT SST/Macro

  5. Feedback-Driven Optimization: Autotuning • Use Ocelot’s dynamic instrumentation capability • Real-Time feedback drives the Ocelot kernel JIT • Decision models to drive existing/new auto-tuners • Change data layout to improve memory efficiency • Use different algorithms • Selective invocation  hot path profiling  algorithm selection Workload Characterization Decision Models Not available with CUPTI Measurements Code Generation

  6. Feedback-Driven Resource Management Applications • Real time customizedinformation available about GPU usage • Can drive scheduling decisions • Can drive management policies, e.g., power, throughput, etc. OCelot Ocelot’s Lynx Management Layer Instrumentation GPU Clusters PTX Instrumented PTX Instrumented PTX Instrumented PTX Instrumentation APIs C-on-Demand JIT Instrumentor C-PTX Translator PTX-PTX Transformer

  7. Domain Specific Compilation: Red Fox Joint with LogicBlox Inc. Datalog Queries LogicBlox Front-End Language Front-End Targeting Accelerator Clouds for meeting the demands of data warehousing applications src-src Optimization Datalog-to-RA (nvcc + RA-Lib) Translation Layer RA Primitives Harmony Kernel IR IR Optimization Harmony Machine Neutral Back-End Ocelot

  8. Thank YouQuestions?

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