Remote Execution Caching Compiler (RECC) CppCon 2024 September 19, 2024 Shivam Bairoliya Software Engineer© 2024 Bloomberg Finance L.P. All rights reserved. What is RECC? ● Remote Execution Caching source build tool that wraps compiler commands and optionally forwards them to a remote build execution service ○ Encompasses the capabilities of both ccache and distcc ○ Supports remote linking and CC) ○ Supports multiple operating systems (Linux, macOS, Solaris) ● Compatible with any remote execution API server supported by Bazel ○ Single Host Server/Proxy: BuildBox-CASD ○ Distributed Server:

r = pixel.g = pixel.b = gray; 5 } 6 } or 1 void to_gray(Image& img) { 2 std::for_each (std::execution::unseq, img.begin(), img.end(), [](Pixel& pixel) { 3 const auto gray = (pixel.r * 11 + pixel.g r = pixel.g = pixel.b = gray; 5 } 6 } or 1 void to_gray(Image& img) { 2 std::for_each (std::execution::unseq, img.begin(), img.end(), [](Pixel& pixel) { 3 const auto gray = (pixel.r * 11 + pixel.g over an image, producing vectorized access to pixels 2 Operations on vectorized pixels • Ideally, the first part is generalized to: Iteration over a range, providing vectorized access to its elements.

ROM Cache L1I • ABI registers implement a SW contract • They do not correspond to the actual execution • Conversion of control flow to a variant of data flow • Really complex • Some operations end of operations are equalExecution 54 Branching Fetch Decode Queue Allocation Scheduling Execution ROM Cache L1I LSQ • Multiple specialized functional units • Performs the actual (eventually) • Latency vs throughputRetirement 55 Branching Fetch Decode Queue Allocation Scheduling Execution ROM Cache L1I L1D LSQ Retirement • All operations until now are speculative • Except some

context (neighboring words), and the label (masked word to be predicted). The word tokens are vectorized by replacing the actual words by their indices in our vocabulary. If a word doesn’t exist in the overfitting. We can now vectorize the train and test datasets. x_train_vectorized = vectorization_layer(x_train) x_test_vectorized = vectorization_layer(x_test) Step 3: Initialization of the Embedding model! bow_model_w2v_history = bow_model_w2v.fit( x_train_vectorized, y_train, batch_size=64, epochs=10, validation_data=(x_test_vectorized, y_test)) Epoch 1/10 313/313 [==============================]

performance. Branch Target Identifiers (BTIs): A BTI instruction is used to guard against the execution of instructions which are not the intended target of a branch. BTIs and PACs combine to reduce Feature Description • Process property settings: The cgroup interface can be called to set the task execution property, either to online or offline. • Preferential tasks: When the CPU is processing online boot time by more than 40% over the Docker+QEMU solution. It provides a lightweight and secure execution environment for applications, isolating security risks in either container and host OS environments

statistical software suite also provides the data set corresponding to the pandas DataFrame. Also SAS vectorized operations, filtering, string processing operations, and more have similar functions in pandas Minimum Ver- sion Notes SciPy 1.7.1 Miscellaneous statistical functions numba 0.53.1 Alternative execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.19.0 pandas-like API for N-dimensional other columns. In pandas, you’re able to do operations on whole columns directly. pandas provides vectorized operations by specifying the individual Series in the DataFrame. New columns can be assigned in

operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 5.5 Vectorized "dot" operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 19.11Array and Vectorized Operators and Functions . . . . . . . . . . . . . . . . . . . . . . . . . . 241 19.12Broadcasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386 34.14More dots: Fuse vectorized operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 34.15Consider using

operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 5.5 Vectorized "dot" operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 19.11Array and Vectorized Operators and Functions . . . . . . . . . . . . . . . . . . . . . . . . . . 241 19.12Broadcasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386 34.14More dots: Fuse vectorized operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 34.15Consider using

operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 17.4 Vectorized "dot" operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 31.11Array and Vectorized Operators and Functions . . . . . . . . . . . . . . . . . . . . . . . . . . 267 CONTENTS vii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408 48.14More dots: Fuse vectorized operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 48.15Consider using

platform ○ Failpoint injection ○ Goroutine-leak detection ● Optimization ○ Chunk vs interface{} ○ Vectorized execution TiDB Overview TiDB TiDB TiDB Application via MySQL Protocol TiKV TiKV TiFlash TiKV TiKV columns[colIdx] return *(*uint64)(unsafe.Pointer(&col.data[r.idx*8])) } Chunk ● Less CPU cache miss ● Vectorized Execute expressions: Chunk func VectorizedExecute(ctx Context, exprs []Expression, iterator End(); row = iterator.Next() { err = executeToInt(ctx, expr, fieldType, row, output, colID) } …. } ● Vectorized Execute expressions: Lessons learned ● Make things right, then make things faster ● Premature