Posts tagged Profiling

TensorFlow Profiler in practice: Optimizing TensorFlow models on AMD GPUs

TensorFlow Profiler consists of a set of tools designed to measure resource utilization and performance during the execution of TensorFlow models. It offers insights into how a model interacts with hardware resources, including execution time and memory usage. TensorFlow Profiler helps in pinpointing performance bottlenecks, allowing us to fine-tune the execution of models for improved efficiency and faster outcomes which can be crucial in scenarios where near-real-time predictions are required.

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AMD in Action: Unveiling the Power of Application Tracing and Profiling

Rocprof is a robust tool designed to analyze and optimize the performance of HIP programs on AMD ROCm platforms, helping developers pinpoint and resolve performance bottlenecks. Rocprof provides a variety of profiling data, including performance counters, hardware traces, and runtime API/activity traces.

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Jacobi Solver with HIP and OpenMP offloading

15 Sept, 2023 by Asitav Mishra, Rajat Arora, Justin Chang.

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Finite difference method - Laplacian part 4

18 Jul, 2023 by Justin Chang, Thomas Gibson, Sean Miller.

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Finite difference method - Laplacian part 3

11 May, 2023 by Justin Chang, Rajat Arora, Thomas Gibson, Sean Miller, Ossian O’Reilly.

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Introduction to profiling tools for AMD hardware

Note: This blog was previously part of the AMD lab notes blog series.

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Finite difference method - Laplacian part 2

4 Jan, 2023 by Justin Chang, Rajat Arora, Thomas Gibson, Sean Miller, Ossian O’Reilly.

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Finite difference method - Laplacian part 1

14 Nov, 2022 by Justin Chang, Rajat Arora, Thomas Gibson, Sean Miller, Ossian O’Reilly.

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