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I can tell I live in a different world—I immediately thought of multi-threaded programming on a single machine. Modern individual machines give us all kinds of

I can tell I live in a different world—I immediately thought of multi-threaded programming on a single machine. Modern individual machines give us all kinds of opportunity for parallel execution—even my phone has vector instructions and two CPU cores. Modern Intel CPUs have 256-bit wide vectors, meaning you can do 8 floating point operations in a single instruction, assuming you can phrase your problem in the right way. If that's not enough, you can throw a pile of these cores at the problem, assuming you can coordinate work between these threads. My experience has been most programmers struggle hard to build multi-threaded applications, countless hours lost to tracking down race conditions, deadlocks, and unexpectedly bad performance, which is a shame. Scanning through this book, it doesn't appear to cover vector instructions, but it does look like it covers coordinating multiple threads in amazing depth. I feel like I already have a decent handle on multi-threaded programming, but I will be reading this book, and I'll probably learn plenty.

shmerl 13y ago
> My experience has been most programmers struggle hard to build multi-threaded applications, countless hours lost to tracking down race conditions, deadlocks, and unexpectedly bad performance, which is a shame. A lot of it is caused by tools which are by design prone to such conditions and unless you consciously and meticulously follow very strict guidelines, such problems are inevitable. But some tools try to solve it with different design approach (such as Rust language for example) which prevents many of those potential pitfalls implicitly. I wonder if the book is focused on shared memory and locking only, or covers broader range of methodologies (at the first glance it's mostly about classic shared memory and mutual exclusion approach).

shmerl 13y ago

> My experience has been most programmers struggle hard to build multi-threaded applications, countless hours lost to tracking down race conditions, deadlocks, and unexpectedly bad performance, which is a shame. A lot of it is caused by tools which are by design prone to such conditions and unless you consciously and meticulously follow very strict guidelines, such problems are inevitable. But some tools try to solve it with different design approach (such as Rust language for example) which prevents many of those potential pitfalls implicitly. I wonder if the book is focused on shared memory and locking only, or covers broader range of methodologies (at the first glance it's mostly about classic shared memory and mutual exclusion approach).