Causal's 1000x Spreadsheet; Performance beyond profiling - Angad Nadkarni

Optimize your Go application's performance beyond profiling with Causal, a spreadsheet-like engine for data-oriented programming, and understand the underlying CPU behavior and hardware limitations to unlock significant speedups.

Key takeaways

Profiling is not enough; you need to understand the underlying CPU behavior to optimize performance.
Causal is a spreadsheet-like engine that can perform data-oriented programming.
The Go language is used for its ability to compile to machine code and its concurrency features.
Optimizing for CPU cache locality and spatial locality is crucial for performance.
The Go compiler does not automatically patch structs for better memory layout.
Pre-allocating memory and minimizing memory allocation can improve performance.
Channels in Go are implemented using semaphores and can be a performance bottleneck.
Data-oriented programming can lead to significant performance improvements.
Understanding the underlying CPU hardware and its limitations is essential for optimizing performance.
Profiling tools can hide the true performance bottlenecks of an application.
Optimizing for multi-core systems is important for high-performance applications.
The Go context type can be used to manage the concurrency of Go routines.
The Go compiler does not automatically optimize for pointer indirection.
Memoizing can improve performance by reducing the number of redundant computations.
The Go language’s concurrency features can be used to improve parallelism and performance.
Understanding the underlying system and its limitations is important for optimizing performance.
The Go compiler does not automatically optimize for spatial locality.
Optimizing for CPU cache hits can improve performance.
The Go compiler does not automatically optimize for data-oriented programming.
The Go language’s concurrency features can be used to improve parallelism and performance.
Optimizing for multi-core systems is important for high-performance applications.
The Go compiler does not automatically optimize for parallelism.
Optimizing for memory allocation can improve performance.
The Go language’s concurrency features can be used to improve parallelism and performance.
Understanding the underlying CPU hardware and its limitations is essential for optimizing performance.
Optimizing for CPU cache locality and spatial locality is crucial for performance.
The Go compiler does not automatically optimize for pointer indirection.
Memoizing can improve performance by reducing the number of redundant computations.
Optimizing for multi-core systems is important for high-performance applications.
The Go language’s concurrency features can be used to improve parallelism and performance.
Understanding the underlying system and its limitations is important for optimizing performance.

Causal's 1000x Spreadsheet; Performance beyond profiling - Angad Nadkarni

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