surprisingly hard to get scope rules correct. Metaprogram- ming should typically be used only when other approaches such as higher order functions and closures cannot be applied. eval and defining new macros should generally rather pointless as a finalizer). This leads us to a bit of a conundrum. There are a few approaches to dealing with this problem: 1. When single-threaded, code could call the internal jl_gc_enable_finalizers not as obvious, hence it can be called an implicit data movement operation. Consider these two approaches to constructing and squaring a random matrix: Method 1: julia> A = rand(1000,1000); julia>

surprisingly hard to get scope rules correct. Metaprogram- ming should typically be used only when other approaches such as higher order functions and closures cannot be applied. eval and defining new macros should generally rather pointless as a finalizer). This leads us to a bit of a conundrum. There are a few approaches to dealing with this problem: 1. When single-threaded, code could call the internal jl_gc_enable_finalizers not as obvious, hence it can be called an implicit data movement operation. Consider these two approaches to constructing and squaring a random matrix: Method 1: julia> A = rand(1000,1000); julia>

surprisingly hard to get scope rules correct. Metaprogram- ming should typically be used only when other approaches such as higher order functions and closures cannot be applied. eval and defining new macros should generally rather pointless as a finalizer). This leads us to a bit of a conundrum. There are a few approaches to dealing with this problem: 1. When single-threaded, code could call the internal jl_gc_enable_finalizers not as obvious, hence it can be called an implicit data movement operation. Consider these two approaches to constructing and squaring a random matrix: Method 1: julia> A = rand(1000,1000); julia>

surprisingly hard to get scope rules correct. Metaprogram- ming should typically be used only when other approaches such as higher order functions and closures cannot be applied. eval and defining new macros should generally rather pointless as a finalizer). This leads us to a bit of a conundrum. There are a few approaches to dealing with this problem: 1. When single-threaded, code could call the internal jl_gc_enable_finalizers not as obvious, hence it can be called an implicit data movement operation. Consider these two approaches to constructing and squaring a random matrix: Method 1: julia> A = rand(1000,1000); julia>

surprisingly hard to get scope rules correct. Metaprogram- ming should typically be used only when other approaches such as higher order functions and closures cannot be applied. eval and defining new macros should generally rather pointless as a finalizer). This leads us to a bit of a conundrum. There are a few approaches to dealing with this problem: 1. When single-threaded, code could call the internal jl_gc_enable_finalizers not as obvious, hence it can be called an implicit data movement operation. Consider these two approaches to constructing and squaring a random matrix: Method 1: julia> A = rand(1000,1000); julia>

surprisingly hard to get scope rules correct. Metaprogramming should typically be used only when other approaches such as higher order functions and closures cannot be applied. eval and defining new macros should generally rather pointless as a finalizer). This leads us to a bit of a conundrum. There are a few approaches to dealing with this problem: 1. When single-threaded, code could call the internal jl_gc_enable_finalizers not as obvious, hence it can be called an implicit data movement operation. Consider these two approaches to constructing and squaring a random matrix: Method 1: julia> A = rand(1000,1000); julia>

surprisingly hard to get scope rules correct. Metaprogramming should typically be used only when other approaches such as higher order functions and closures cannot be applied. eval and defining new macros should generally rather pointless as a finalizer). This leads us to a bit of a conundrum. There are a few approaches to dealing with this problem: 1. When single-threaded, code could call the internal jl_gc_enable_finalizers not as obvious, hence it can be called an implicit data movement operation. Consider these two approaches to constructing and squaring a random matrix: Method 1: julia> A = rand(1000,1000); julia>

surprisingly hard to get scope rules correct. Metaprogramming should typically be used only when other approaches such as higher order functions and closures cannot be applied. eval and defining new macros should generally rather pointless as a finalizer). This leads us to a bit of a conundrum. There are a few approaches to dealing with this problem: 1. When single-threaded, code could call the internal jl_gc_enable_finalizers not as obvious, hence it can be called an implicit data movement operation. Consider these two approaches to constructing and squaring a random matrix: Method 1: julia> A = rand(1000,1000); julia>

surprisingly hard to get scope rules correct. Metaprogramming should typically be used only when other approaches such as higher order functions and closures cannot be applied. eval and defining new macros should generally rather pointless as a finalizer). This leads us to a bit of a conundrum. There are a few approaches to dealing with this problem: 1. When single-threaded, code could call the internal jl_gc_enable_finalizers not as obvious, hence it can be called an implicit data movement operation. Consider these two approaches to constructing and squaring a random matrix: Method 1: julia> A = rand(1000,1000); julia>

surprisingly hard to get scope rules correct. Metaprogramming should typically be used only when other approaches such as higher order functions and closures cannot be applied. eval and defining new macros should generally rather pointless as a finalizer). This leads us to a bit of a conundrum. There are a few approaches to dealing with this problem: 1. When single-threaded, code could call the internal jl_gc_enable_finalizers not as obvious, hence it can be called an implicit data movement operation. Consider these two approaches to constructing and squaring a random matrix: Method 1: julia> A = rand(1000,1000); julia>