How deeply does Deep Learning with R, Third Edition cover XLA?

Explains the compilation switch and startup cost, but stops short of compiler internals.

XLA

As explained in Deep Learning with R, Third Edition

XLA is a compiler for accelerating linear algebra and machine-learning computations across supported hardware.

XLA is a high-performance compiler for machine-learning computations. Enable it after debugging in the R runtime: compilation can improve execution speed, but the first call takes longer while the function is compiled; later calls reuse it. section 3.3.1

Compile a dense operation

dense <- tf_function(jit_compile = TRUE, \\(inputs, W, b) {\n tf$nn$relu(tf$matmul(inputs, W) + b)\n}) section 3.3.1

dense <- tf_function(jit_compile = TRUE, \(inputs, W, b) {
  tf$nn$relu(tf$matmul(inputs, W) + b)
})

This listing shows the R syntax for enabling XLA compilation on a TensorFlow function. section 3.3.1

How Deep Learning with R, Third Edition covers it

Cover of Deep Learning with R, Third Edition

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Also explained in

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Deep Learning with JAXA compiler and runtime for linear algebra and array programs that optimizes computations such as those produced by JAX.
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Deep Learning with Python, Third EditionAccelerated Linear Algebra, TensorFlow's optional compiler used to further optimize and speed up function execution.

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Related concepts

JAXused by · JAX is a Python library for high-performance array-based numerical computing with composable transformations including automatic differentiation, compilation, vectorization, and parallelization.
just-in-time compilationused by · Just-in-time compilation translates or optimizes program code during execution, often using runtime information to generate specialized executable instructions.
parallel mapused by · A parallel map is an operation that applies a computation concurrently across inputs or processing devices and returns the corresponding results.
Jaxprused by · JAX's simple intermediate representation of a traced function, used as the input to later transformations and compilation.
kernel fusionaddresses · Kernel fusion is an optimization that combines multiple computational operations into fewer executable units to reduce launch overhead and intermediate data movement.
TPUuses · A Tensor Processing Unit (TPU) is a specialized processor designed to accelerate tensor computations, particularly for machine-learning workloads.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with R, Third Edition. The section links point to the parts of the book it draws on.