I am moving my blog away from blogspot / blogger. I am going to host my new blog at micro.blog. You can subscribe to an RSS feed on Lisp-related posts if you care only for that. micro.blog also acts as a social network and, although it is its own platform, is compatible with Mastodon. My Mastodon handle is @costanza@micro.blog.
November 03, 2022
January 29, 2021
The Slick programming language
I’m happy to announce the release of the Slick programming language, an s-expression surface syntax for Go, with some extensions inspired by Common Lisp and Scheme. See the Slick programming language repository for more details.
February 28, 2016
Closer project repositories moved to github
I have moved the Closer project repositories to github. All details around this move are listed at the Closer project website, especially see the list of repositories. Since github provides dedicated means for reporting issues, I’m shutting down the mailing lists for the Closer project as well.
September 07, 2014
"Why I like Common Lisp"
In a recent email exchange discussion, Charlotte Herzeel gave a summary of Common Lisp that I believe is worth repeating publicly. With her permission, I repeat her statements here.
“An important reason why I like Common Lisp a lot is that the language has a layered design that supports incremental development. The language provides very high-level programming abstractions, such as object-oriented programming, dynamic multiple dispatch, garbage collection, a meta-object protocol, and so on. These abstractions are typically open implementations, built on top of more efficient low-level abstractions the user can also choose to access directly.
Common Lisp is typically implemented as a compiled language, compiling directly to machine code. The runtime components are sparse, the garbage collector being an important one. Common Lisp provides the means to steer the compiler and runtime components to do low-level optimizations. Examples of this include: type declarations to remove type-checking at runtime; inline declarations to avoid dispatch; dynamic extent declarations to perform stack allocation instead of heap allocation; disassembly of code snippets; tuning of the garbage collector to switch between collection strategies; and so on. Optimizations such as these are optional and localized. Hence it is very easy in Common Lisp to rapidly prototype and then incrementally optimize the code by identifying the hotspots through profiling. This way you can often be as efficient as with C code, without being forced to program in a low-level style from the start throughout your whole program.
Hence in contrast to C/C++, Common Lisp allows you to optimize code incrementally and locally for a particular snippet of code. In contrast to Java - or any other language with an implementation that performs optimization at runtime through tracing or JIT compiling or so - Common Lisp implementations employ in a sense a more classic compilation approach. In this sense, Common Lisp makes it easier to ‘control’ what you are measuring when profiling programs.
The Common Lisp Object System (CLOS) is a library in Common Lisp for object-oriented programming. Common Lisp is a multi-paradigm language, so it depends on your problem whether it is a good idea to use object-oriented programming or not. That said, CLOS is very different from mainstream object-oriented programming. It allows multiple inheritance, multiple dispatch, and is based on generic functions, i.e. classes define types, and methods are defined separately as part of generic functions. The CLOS implementation performs a lot of clever optimizations at runtime, for example for method lookup. What is of course special about CLOS, is that it has a meta-object protocol, which allows you to extend/modify CLOS in an organized way. For example, you have hooks into the method dispatch protocol, the slot (= field) access protocol, etc. If you want to know more about the CLOS implementation and the meta-object protocol, read ‘The Art of the Meta-Object Protocol’ by Kiczales, des Rivieres, Bobrow.
Common Lisp just has a lot of advanced language features that you just don’t find in other languages.
From a practical point of view, I can recommend LispWorks as a Common Lisp implementation. LispWorks is very user-friendly because it comes with an integrated development environment. This means you get Smalltalk-like features such as code browsers and inspector tools. Another user-friendly implementation that is free is Clozure Common Lisp. The most widely used open-source implementation is SBCL, which is very stable and very efficient. There are lots of other Common Lisp implementations out there, but I recommend one of these three.
If you want to learn about Common Lisp, I can recommend “Ansi Common Lisp” by Graham. Maybe also interesting: ‘Pascal Costanza’s highly opinionated guide to Common Lisp’ ;-). If you want a funny introduction to Common Lisp, check out the Lisperati. A good place to snoop for Common Lisp war stories is Planet Lisp. If you want to get an idea about libraries, see quicklisp.”
“An important reason why I like Common Lisp a lot is that the language has a layered design that supports incremental development. The language provides very high-level programming abstractions, such as object-oriented programming, dynamic multiple dispatch, garbage collection, a meta-object protocol, and so on. These abstractions are typically open implementations, built on top of more efficient low-level abstractions the user can also choose to access directly.
Common Lisp is typically implemented as a compiled language, compiling directly to machine code. The runtime components are sparse, the garbage collector being an important one. Common Lisp provides the means to steer the compiler and runtime components to do low-level optimizations. Examples of this include: type declarations to remove type-checking at runtime; inline declarations to avoid dispatch; dynamic extent declarations to perform stack allocation instead of heap allocation; disassembly of code snippets; tuning of the garbage collector to switch between collection strategies; and so on. Optimizations such as these are optional and localized. Hence it is very easy in Common Lisp to rapidly prototype and then incrementally optimize the code by identifying the hotspots through profiling. This way you can often be as efficient as with C code, without being forced to program in a low-level style from the start throughout your whole program.
Hence in contrast to C/C++, Common Lisp allows you to optimize code incrementally and locally for a particular snippet of code. In contrast to Java - or any other language with an implementation that performs optimization at runtime through tracing or JIT compiling or so - Common Lisp implementations employ in a sense a more classic compilation approach. In this sense, Common Lisp makes it easier to ‘control’ what you are measuring when profiling programs.
The Common Lisp Object System (CLOS) is a library in Common Lisp for object-oriented programming. Common Lisp is a multi-paradigm language, so it depends on your problem whether it is a good idea to use object-oriented programming or not. That said, CLOS is very different from mainstream object-oriented programming. It allows multiple inheritance, multiple dispatch, and is based on generic functions, i.e. classes define types, and methods are defined separately as part of generic functions. The CLOS implementation performs a lot of clever optimizations at runtime, for example for method lookup. What is of course special about CLOS, is that it has a meta-object protocol, which allows you to extend/modify CLOS in an organized way. For example, you have hooks into the method dispatch protocol, the slot (= field) access protocol, etc. If you want to know more about the CLOS implementation and the meta-object protocol, read ‘The Art of the Meta-Object Protocol’ by Kiczales, des Rivieres, Bobrow.
Common Lisp just has a lot of advanced language features that you just don’t find in other languages.
From a practical point of view, I can recommend LispWorks as a Common Lisp implementation. LispWorks is very user-friendly because it comes with an integrated development environment. This means you get Smalltalk-like features such as code browsers and inspector tools. Another user-friendly implementation that is free is Clozure Common Lisp. The most widely used open-source implementation is SBCL, which is very stable and very efficient. There are lots of other Common Lisp implementations out there, but I recommend one of these three.
If you want to learn about Common Lisp, I can recommend “Ansi Common Lisp” by Graham. Maybe also interesting: ‘Pascal Costanza’s highly opinionated guide to Common Lisp’ ;-). If you want a funny introduction to Common Lisp, check out the Lisperati. A good place to snoop for Common Lisp war stories is Planet Lisp. If you want to get an idea about libraries, see quicklisp.”
July 20, 2014
A Lisper's first impression of Julia
I have recently looked at Julia, a new programming language developed at MIT that promises to be a dynamic programming language that is suitable for scientific computing with a high-performance implementation. It is an interesting project that heavily borrows from Common Lisp, Dylan, and Scheme, and you can rightfully argue that Julia itself is actually a Lisp dialect. While I wasn’t very impressed with some other recent Lisp dialects (Arc, Clojure), Julia puts a couple of very interesting features on the table. Below is a discussion of the more salient aspects of Julia, as seen from a Common Lispers perspective. It is based on the documentation of the prerelease version 0.3 of Julia.
Julia is closest to Dylan in many regards. It uses a somewhat mainstream syntax rather than s-expressions. Unlike Dylan, you can nevertheless write ‘full’ macros, since macro definitions are implemented in Julia, not some template language, and backquote/quasiquote is integrated with the Julia syntax. Julia is a Lisp-1 (like Scheme or Dylan) rather than a Lisp-2 (like Common Lisp or ISLISP), which makes it necessary to add macro hygiene features. Fortunately, this does not mean you have to deal with the rather painful syntax-case construct of some Scheme dialects, but you can still use far simpler backquote/quasiquote constructions, just with macro hygiene taken care of by default. Julia also allows you to selectively break hygiene. Although I usually strongly prefer the simplicity of Common Lisp’s non-hygienic macro system, the fact that Julia is a Lisp-1 turns macro hygiene into a real problem, so I guess this is a reasonable design.
Julia provides object-oriented programming from the ground up, similar to Dylan. It centers on generic functions rather than classes, where methods are defined outside classes and allow for multiple dispatch, just like in Dylan and Common Lisp. Like Dylan, and unlike Common Lisp, it does not distinguish between functions and generic functions: All functions can have methods, you do not have to make up your mind whether you want methods or plain functions. Unlike in Common Lisp, there are no method combinations, no before/after/around methods, and call-next-method is not directly supported, but has to be done manually. This is probably to simplify method dispatch, maybe to have some performance advantages, though I find it hard to imagine that adding method combinations would make things substantially worse.
You still need a class hierarchy to drive method dispatch. Unlike in Common Lisp, there is no multiple inheritance, only single inheritance. In fact, there is actually no real inheritance, because in Julia, only leaf classes of the class hierarchy are allowed to define slots/fields. All superclasses are required to be “abstract,” without any slot definitions. Also, Julia classes cannot be redefined at runtime, so in fact Julia classes are much closer to Common Lisp’s structured types rather than classes.
Julia’s execution model is based on dynamic compilation. As a user, you don’t have to compile your code at all, source code is just compiled on the fly (similar as in Clozure Common Lisp). Julia inlines functions on the fly, including generic functions, and can de-optimize when function definitions change at runtime. This is more flexible than in Common Lisp, where inlined functions can get out of sync with their potentially changed definitions. Also, while the Common Lisp specification does not say anything with regard to being able to inline generic functions or not, there are aspects in the CLOS MOP specification that prevent generic functions from being inlined, at least for user-defined extensions of generic functions. Julia definitely seems more “modern” here.
In Julia, there is no distinction between variable binding and variable assignment. If you assign to a variable that has not been used before in the same lexical environment, it is silently introduced. In Common Lisp/Scheme/Dylan, there is a distinction between ‘let forms that introduce variable bindings, and assignments (setq/setf/set!) that perform assignments. I’m highly skeptical of Julia’s design here, because this potentially leads to bugs that are hard to find: A simple typo in your source code just may go unnoticed.
In Julia, all variables are lexically scoped (except for some seemingly quirky scoping semantics for global variables, see below). There are no special / dynamically scoped variables in Julia, which is a major omission in my book. Some academics don’t like special scoping, but their presence in Common Lisp is incredibly useful in practice, especially but not only for multi-threading!
Julia’s default representation for integers is either 32-bit or 64-bit integers, depending on the target architecture, which silently wrap around. Julia also supports “BigInts” that can be arbitrarily large, but you have to ask for them explicitly. In Common Lisp, integers are by default arbitrarily large, which I think is an advantage. Due to type tagging in Common Lisp implementations, even “big” integers are typically allocated as immediate values rather than on the heap when they fall into the “fixnum” range. I didn’t find anything in the Julia documentation that discusses this aspect of “BigInt.”
In Julia, all mathematical operations are generic functions and can be extended by user-defined methods. This is a strong advantage for Julia. In Common Lisp, mathematical operations are “plain” functions which cannot be extended. Due to some aspects in the design of Common Lisp’s generic functions, it’s hard to inline (or open-code) them, which is why for performance reasons, it’s better to express mathematical (and other such performance-critical functions) as “plain” functions. Apart from that, the support for number types seems to be on par between Julia and Common Lisp (complex types, rational numbers, floating point numbers, etc.)
In Julia, strings are unicode strings by default. My knowledge about unicode support in Common Lisp implementations is limited, so I cannot really make any comparisons here. One interesting aspect in Julia’s string support is that there can be user-defined macros to parse them and construct other syntactic entities out of them. This feels somewhat similar to read macros in Common Lisp, although with a slightly different scope.
Julia’s support for functions is similar to Common Lisp: They can be first class and anonymous (lambda expressions). There are varargs (&rest), optional and keyword arguments. In Common Lisp, optional and keyword arguments cannot be dispatched on in methods. In Julia, optional arguments can be dispatched on, but not keywords. (This is a pity, dispatch on keyword arguments would be very interesting, and is something I wanted to add as part of Closer to MOP for a long time!)
Julia’s support for control flow is much more limited than in Common Lisp. There are equivalents for progn, cond/if, for and while loops. Unlike Common Lisp, there is no support for a full loop facility, or even for a simple goto construct. Common Lisp clearly wins here. Julia’s support for exception handling is also limited: No handler-bind, no restarts, unlike in Common Lisp, which are also really useful features.
Julia’s type system has some interesting differences to Common Lisp: There is a distinction between mutable and immutable classes. Immutable classes disallow any side effects on their fields. This seems to be primarily directed at enabling stack allocation as an optimization. In Common Lisp, you would use dynamic extent declarations when allocating structs (or other data types) to achieve similar performance improvements. I’m not sure why it would matter that the fields need to be read-only for such an optimization, but if this covers most cases, maybe this is good enough.
Julia allows for returning and receiving multiple values from function calls, similar to Common Lisp. This is a feature I like a lot in Common Lisp, so I’m happy it’s also in Julia. In Julia, this is achieved by an explicit tuple type representation which doesn’t exist in this form in Common Lisp. In Lisp, you could also return/receive lists instead of multiple values, which would correspond to this kind of tuple type, but lists add an additional performance overhead, which multiple values and, presumably, tuples in Julia don’t have.
Julia supports parametric types. I don’t see at the moment why this is relevant. You could achieve similar functionality also with some macrology. Maybe I’m missing something here.
There is a whole section on constructors (make-instance / make-struct) in the Julia manual. This makes me suspicious, this should be an easier topic.
Julia has a module system. It supports export and automatic import of explicitly exported identifiers. You can also still access non-exported identifiers with additional syntax. This is good, because module designers may not always perfectly anticipate what users actually need to access. Common Lisp’s package system supports a similar distinction between external and internal definitions that can be accessed in different ways. I slightly prefer Common Lisp’s ability to use the package name as a prefix even for explicitly exported definitions. There is no feature in Julia to rename imported identifiers, which is something where Common Lisp’s support for explicit package name prefixes can come in very handy. I would like to see something like Oberon’s support for renaming identifiers on import in some Lisp dialect someday, because I believe that is the most complete solution for dealing with potential name conflicts.
In terms of meta-programming, apart from macros, Julia also supports (top-level) eval, like Common Lisp. Julia’s support for “reflection” is much weaker than Common Lisp’s CLOS MOP: You can only inspect types at runtime, but you cannot modify them (and language designers should stop calling something “reflection” that is clearly just “introspection”).
Both Julia and Common Lisp support multi-dimensional arrays. Common Lisp’s arrays are in row-major order, starting at index 0 in every dimension. Julia’s arrays are column-major order, starting at index 1 in every dimension. Julia’s support for multi-dimensional arrays is a library feature, whereas Common Lisp’s support is built into the language. Julia supports both dense and sparse matrices, where Common Lisp supports only dense matrices out of the box.
There are a lot of libraries that ship with Julia targeted at scientific computing.
Julia supports parallel programming with a model built on top of message passing: If you want to run parallel algorithms, you essentially start several instances of Julia that communicate with each other. The model does not support shared memory, and there is no multi-threading within a Julia instance (although there seem to be discussions among the Julia designers to add this in the future). The model is built on top of MPI as an implementation backend. However, the actual programming model supports single-sided communication: You can ask a function to be executed in some other Julia worker process, and can later synchronize with it to fetch results. On top of that, there are some high-level constructs provided as library features, such parallel maps and loops. Julia’s message passing model ensures that within a Julia instance, only one task is executed at a time, so there is yet no need to provide low-level synchronization mechanisms, such as locks or atomic operations. The lack of shared-memory parallelism is problematic because many parallel algorithms that are very easy to express with shared memory become quite complicated in a distributed memory setting. On the other hand, Julia’s model easily supports true distributed programming: You can configure Julia to run several instances across a cluster, and use them in a quite straightforward way: substantially easier than what you have to do with, say, MPI, and much closer with regard to ease of use to modern PGAS languages like Chapel or X10.
The ANSI specification for Common Lisp does not mention anything about multi-threading or parallel programming at all, but many Common Lisp implementations add support for shared-memory parallelism. I will not go into details here, but let me just briefly state that, for example, the LispWorks implementation of Common Lisp provides excellent support for symmetric multiprocessing that is at least on par with what you can find in most other language implementations in terms of parallel programming support. However, unfortunately, support for true distributed memory models seems almost non-existent in Common Lisp, apart for some basic support for MPI in a library that was maintained only for a very short period of time a couple of years ago. Julia looks like a good source of inspiration for adding such features to Common Lisp.
However, one aspect of Julia’s message passing approach seems problematic, as far as I can tell: You can pass closures between different instances of Julia, but it’s not clear how free variables in a lambda expression are bound. It seems that lexical variables are bound and serialized to another process, but global variables are not serialized and need to be present in any presence that may execute the closure. Experiments with adding side effects to free variables in lambda expressions that are passed to other processes seem to suggest that the semantics of this combination of features are unpredictable. At least, I have not been able to figure out what happens when — sometimes variables seem to be updated at the sender’s side, sometimes not — and I didn’t find a discussion of this topic in the Julia manual.
Anyway, as you can tell, there are a lot of interesting features in Julia, and it’s definitely worth a try.
Julia is closest to Dylan in many regards. It uses a somewhat mainstream syntax rather than s-expressions. Unlike Dylan, you can nevertheless write ‘full’ macros, since macro definitions are implemented in Julia, not some template language, and backquote/quasiquote is integrated with the Julia syntax. Julia is a Lisp-1 (like Scheme or Dylan) rather than a Lisp-2 (like Common Lisp or ISLISP), which makes it necessary to add macro hygiene features. Fortunately, this does not mean you have to deal with the rather painful syntax-case construct of some Scheme dialects, but you can still use far simpler backquote/quasiquote constructions, just with macro hygiene taken care of by default. Julia also allows you to selectively break hygiene. Although I usually strongly prefer the simplicity of Common Lisp’s non-hygienic macro system, the fact that Julia is a Lisp-1 turns macro hygiene into a real problem, so I guess this is a reasonable design.
Julia provides object-oriented programming from the ground up, similar to Dylan. It centers on generic functions rather than classes, where methods are defined outside classes and allow for multiple dispatch, just like in Dylan and Common Lisp. Like Dylan, and unlike Common Lisp, it does not distinguish between functions and generic functions: All functions can have methods, you do not have to make up your mind whether you want methods or plain functions. Unlike in Common Lisp, there are no method combinations, no before/after/around methods, and call-next-method is not directly supported, but has to be done manually. This is probably to simplify method dispatch, maybe to have some performance advantages, though I find it hard to imagine that adding method combinations would make things substantially worse.
You still need a class hierarchy to drive method dispatch. Unlike in Common Lisp, there is no multiple inheritance, only single inheritance. In fact, there is actually no real inheritance, because in Julia, only leaf classes of the class hierarchy are allowed to define slots/fields. All superclasses are required to be “abstract,” without any slot definitions. Also, Julia classes cannot be redefined at runtime, so in fact Julia classes are much closer to Common Lisp’s structured types rather than classes.
Julia’s execution model is based on dynamic compilation. As a user, you don’t have to compile your code at all, source code is just compiled on the fly (similar as in Clozure Common Lisp). Julia inlines functions on the fly, including generic functions, and can de-optimize when function definitions change at runtime. This is more flexible than in Common Lisp, where inlined functions can get out of sync with their potentially changed definitions. Also, while the Common Lisp specification does not say anything with regard to being able to inline generic functions or not, there are aspects in the CLOS MOP specification that prevent generic functions from being inlined, at least for user-defined extensions of generic functions. Julia definitely seems more “modern” here.
In Julia, there is no distinction between variable binding and variable assignment. If you assign to a variable that has not been used before in the same lexical environment, it is silently introduced. In Common Lisp/Scheme/Dylan, there is a distinction between ‘let forms that introduce variable bindings, and assignments (setq/setf/set!) that perform assignments. I’m highly skeptical of Julia’s design here, because this potentially leads to bugs that are hard to find: A simple typo in your source code just may go unnoticed.
In Julia, all variables are lexically scoped (except for some seemingly quirky scoping semantics for global variables, see below). There are no special / dynamically scoped variables in Julia, which is a major omission in my book. Some academics don’t like special scoping, but their presence in Common Lisp is incredibly useful in practice, especially but not only for multi-threading!
Julia’s default representation for integers is either 32-bit or 64-bit integers, depending on the target architecture, which silently wrap around. Julia also supports “BigInts” that can be arbitrarily large, but you have to ask for them explicitly. In Common Lisp, integers are by default arbitrarily large, which I think is an advantage. Due to type tagging in Common Lisp implementations, even “big” integers are typically allocated as immediate values rather than on the heap when they fall into the “fixnum” range. I didn’t find anything in the Julia documentation that discusses this aspect of “BigInt.”
In Julia, all mathematical operations are generic functions and can be extended by user-defined methods. This is a strong advantage for Julia. In Common Lisp, mathematical operations are “plain” functions which cannot be extended. Due to some aspects in the design of Common Lisp’s generic functions, it’s hard to inline (or open-code) them, which is why for performance reasons, it’s better to express mathematical (and other such performance-critical functions) as “plain” functions. Apart from that, the support for number types seems to be on par between Julia and Common Lisp (complex types, rational numbers, floating point numbers, etc.)
In Julia, strings are unicode strings by default. My knowledge about unicode support in Common Lisp implementations is limited, so I cannot really make any comparisons here. One interesting aspect in Julia’s string support is that there can be user-defined macros to parse them and construct other syntactic entities out of them. This feels somewhat similar to read macros in Common Lisp, although with a slightly different scope.
Julia’s support for functions is similar to Common Lisp: They can be first class and anonymous (lambda expressions). There are varargs (&rest), optional and keyword arguments. In Common Lisp, optional and keyword arguments cannot be dispatched on in methods. In Julia, optional arguments can be dispatched on, but not keywords. (This is a pity, dispatch on keyword arguments would be very interesting, and is something I wanted to add as part of Closer to MOP for a long time!)
Julia’s support for control flow is much more limited than in Common Lisp. There are equivalents for progn, cond/if, for and while loops. Unlike Common Lisp, there is no support for a full loop facility, or even for a simple goto construct. Common Lisp clearly wins here. Julia’s support for exception handling is also limited: No handler-bind, no restarts, unlike in Common Lisp, which are also really useful features.
Julia’s type system has some interesting differences to Common Lisp: There is a distinction between mutable and immutable classes. Immutable classes disallow any side effects on their fields. This seems to be primarily directed at enabling stack allocation as an optimization. In Common Lisp, you would use dynamic extent declarations when allocating structs (or other data types) to achieve similar performance improvements. I’m not sure why it would matter that the fields need to be read-only for such an optimization, but if this covers most cases, maybe this is good enough.
Julia allows for returning and receiving multiple values from function calls, similar to Common Lisp. This is a feature I like a lot in Common Lisp, so I’m happy it’s also in Julia. In Julia, this is achieved by an explicit tuple type representation which doesn’t exist in this form in Common Lisp. In Lisp, you could also return/receive lists instead of multiple values, which would correspond to this kind of tuple type, but lists add an additional performance overhead, which multiple values and, presumably, tuples in Julia don’t have.
Julia supports parametric types. I don’t see at the moment why this is relevant. You could achieve similar functionality also with some macrology. Maybe I’m missing something here.
There is a whole section on constructors (make-instance / make-struct) in the Julia manual. This makes me suspicious, this should be an easier topic.
Julia has a module system. It supports export and automatic import of explicitly exported identifiers. You can also still access non-exported identifiers with additional syntax. This is good, because module designers may not always perfectly anticipate what users actually need to access. Common Lisp’s package system supports a similar distinction between external and internal definitions that can be accessed in different ways. I slightly prefer Common Lisp’s ability to use the package name as a prefix even for explicitly exported definitions. There is no feature in Julia to rename imported identifiers, which is something where Common Lisp’s support for explicit package name prefixes can come in very handy. I would like to see something like Oberon’s support for renaming identifiers on import in some Lisp dialect someday, because I believe that is the most complete solution for dealing with potential name conflicts.
In terms of meta-programming, apart from macros, Julia also supports (top-level) eval, like Common Lisp. Julia’s support for “reflection” is much weaker than Common Lisp’s CLOS MOP: You can only inspect types at runtime, but you cannot modify them (and language designers should stop calling something “reflection” that is clearly just “introspection”).
Both Julia and Common Lisp support multi-dimensional arrays. Common Lisp’s arrays are in row-major order, starting at index 0 in every dimension. Julia’s arrays are column-major order, starting at index 1 in every dimension. Julia’s support for multi-dimensional arrays is a library feature, whereas Common Lisp’s support is built into the language. Julia supports both dense and sparse matrices, where Common Lisp supports only dense matrices out of the box.
There are a lot of libraries that ship with Julia targeted at scientific computing.
Julia supports parallel programming with a model built on top of message passing: If you want to run parallel algorithms, you essentially start several instances of Julia that communicate with each other. The model does not support shared memory, and there is no multi-threading within a Julia instance (although there seem to be discussions among the Julia designers to add this in the future). The model is built on top of MPI as an implementation backend. However, the actual programming model supports single-sided communication: You can ask a function to be executed in some other Julia worker process, and can later synchronize with it to fetch results. On top of that, there are some high-level constructs provided as library features, such parallel maps and loops. Julia’s message passing model ensures that within a Julia instance, only one task is executed at a time, so there is yet no need to provide low-level synchronization mechanisms, such as locks or atomic operations. The lack of shared-memory parallelism is problematic because many parallel algorithms that are very easy to express with shared memory become quite complicated in a distributed memory setting. On the other hand, Julia’s model easily supports true distributed programming: You can configure Julia to run several instances across a cluster, and use them in a quite straightforward way: substantially easier than what you have to do with, say, MPI, and much closer with regard to ease of use to modern PGAS languages like Chapel or X10.
The ANSI specification for Common Lisp does not mention anything about multi-threading or parallel programming at all, but many Common Lisp implementations add support for shared-memory parallelism. I will not go into details here, but let me just briefly state that, for example, the LispWorks implementation of Common Lisp provides excellent support for symmetric multiprocessing that is at least on par with what you can find in most other language implementations in terms of parallel programming support. However, unfortunately, support for true distributed memory models seems almost non-existent in Common Lisp, apart for some basic support for MPI in a library that was maintained only for a very short period of time a couple of years ago. Julia looks like a good source of inspiration for adding such features to Common Lisp.
However, one aspect of Julia’s message passing approach seems problematic, as far as I can tell: You can pass closures between different instances of Julia, but it’s not clear how free variables in a lambda expression are bound. It seems that lexical variables are bound and serialized to another process, but global variables are not serialized and need to be present in any presence that may execute the closure. Experiments with adding side effects to free variables in lambda expressions that are passed to other processes seem to suggest that the semantics of this combination of features are unpredictable. At least, I have not been able to figure out what happens when — sometimes variables seem to be updated at the sender’s side, sometimes not — and I didn’t find a discussion of this topic in the Julia manual.
Anyway, as you can tell, there are a lot of interesting features in Julia, and it’s definitely worth a try.
September 06, 2013
August 18, 2013
The Human Consequences of Dynamic Typing
I agree with Jay McCarthy that static typing is anti-human. I think I can give a slightly more elaborate example of a program that every static type checker must reject, but that is nevertheless correct. Everything below is valid Common Lisp:
(defclass person ()
((name :initarg :name
:accessor person-name)))
(defmethod display ((p person))
(format t "Person: Name ~S, address ~S."
(person-name p)
(person-address p)))
A static type checker must reject this program because there is no address field defined in the class person that person-address presumably refers to. However, below is a run of the program in a Lisp listener that runs to a correct completion without fatal (!) errors:
CL-USER 1 > (defvar *p* (make-instance 'person :name "Pascal")) *P*
CL-USER 2 > (display *p*)
Error: Undefined function PERSON-ADDRESS called with arguments
(#<PERSON 4020059AB3>).
1 (continue) Try invoking PERSON-ADDRESS again.
2 Return some values from the call to PERSON-ADDRESS.
3 Try invoking something other than PERSON-ADDRESS with the
same arguments.
4 Set the symbol-function of PERSON-ADDRESS to another
function.
5 (abort) Return to level 0.
6 Return to top loop level 0.
Type :b for backtrace or :c <option number> to proceed. Type :bug-form "<subject>" for a bug report template or :? for other options.
CL-USER 3 : 1 > (defclass person ()
((name :initarg :name
:accessor person-name)
(address :initarg :address
:accessor person-address)))
#<STANDARD-CLASS PERSON 4130371F6B>
CL-USER 4 : 1 > :c 1
Error: The slot ADDRESS is unbound in the object #<PERSON 41303DD973> (an instance of class #<STANDARD-CLASS PERSON 4130371F6B>). 1 (continue) Try reading slot ADDRESS again. 2 Specify a value to use this time for slot ADDRESS. 3 Specify a value to set slot ADDRESS to. 4 (abort) Return to level 0. 5 Return to top loop level 0.
Type :b for backtrace or :c <option number> to proceed. Type :bug-form "<subject>" for a bug report template or :? for other options.
CL-USER 5 : 1 > :c 3
Enter a form to be evaluated: "Belgium" Person: Name "Pascal", address "Belgium". NIL
CL-USER 6 > (display *p*) Person: Name "Pascal", address "Belgium". NIL
The "trick" is that the Lisp listener allows for interactive modification of a program while it is running. No static type checker can anticipate what modifications will be performed at runtime.
This is not a toy feature of Common Lisp, but something that many Lisp developers rely on. For example, my own ContextL library crucially relies on the very class redefinition feature I demonstrate above. Joe Marshall provides another account how such features can solve real-world problems.
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