Alternatives to C/C++ for system programming in a distributed multicore world
The distributed, multicore train is stopping for no programmer, and especially the systems programmer will need to be ready to hop on to the distributed parallel programming paradigm to keep their systems running as efficiently as possible on the latest hardware environments.
There are three new systems programming languages that have appeared in the last few years which are attempting to provide a safe, productive, and efficient parallel programming capability. Go is a new language from Google , Rust is a new language from Mozilla , and ParaSail is a new language from AdaCore .
The designers of Go, Rust, and ParaSail all are facing a common challenge -- how to help programmers address the new distributed and multicore architectures, without having the complexity of programming going past that which is manageable by the professional, yet still human, programmer. All programming languages evolve, and as a rule, they tend to get more complex, not less so.
If every time a new hardware architecture becomes important, the programming language is enhanced to provide better support for that architecture, the language can become totally unwieldy, even for the best programmers.
When the architectures changes radically, as with the new massively distributed and/or multicore/manycore architectures, this may mean that the language no longer hangs together at all, and instead has become a federation of sublanguages, much like a house that has been added onto repeatedly with a different style for each addition.
Because of the complexity curse associated with language evolution, when there is a significant shift in the hardware landscape, there is a strong temptation to start over in programming language design. After many years of a relatively stable programming language world, we now see a new burst of activity on the language design front, inspired in large part by the sense that our existing mainstream languages are either not going to be supportive enough, or that they are becoming simply too complex in trying to support both the old and new hardware paradigms through a series of language extensions.
Go from Google
Go, Rust, and ParaSail all emerged over the past few years, each with its own approach to managing complexity while supporting parallelism. Go from Google is the brain child of Rob Pike and his colleagues at Google. Rob was at Bell Labs in the early Unix and C days, and in many ways Go inherits the C tradition of simplicity and power. Unlike C, storage management has been taken over by the Go run-time through a general purpose garbage collection approach, but like C, care is still needed in other areas to ensure overall program safety.
From the multicore perspective, Go uses goroutines for structuring large computations into a set of smaller potentially parallel computations. Goroutines are easy to create – essentially any stand-alone call on a function or a method can be turned into a goroutine by simply prefixing it with the word “go.” Once a goroutine is spawned, it executes independently of the rest of the code. A goroutine is allowed to outlive the function that spawns it, thanks to the support of the garbage collector; local variables of the spawning function will live as long as necessary if they are visible to the spawned goroutine.
For goroutines to be useful, they need to communicate their re- sults back to the spawning routine. This is generally done using strongly-typed channels in Go. A channel can be passed as a parameter as part of spawning a goroutine, and then as the goroutine performs its computation it can send one or more results into the channel.
Meanwhile, at some appropriate point after spawning the goroutine, the spawner can attempt to receive one or more values from the channel. A channel can be unbuffered, providing a synchronous communication between sender and receiver, or it can provide a buffer of a specified size, effectively creating a message queue.
Communication between go routines can also go directly through shared global variables. However, some sort of synchronization through channels or explicit locks is required to ensure that the shared variables are updated and read in an appropriate sequence.
Here is an example Go program that counts the number of words in a string, presuming they are separated by one or more separator characters, dividing multi-character strings in half and passing them off to goroutines for recursive word counts:
Unique features of Go. Go has some unusual features. Whether a declaration is exported is determined by whether or not its name begins with an upper- case letter (as defined by Unicode); if the declaration is a pack- age-level declaration or is the declaration of a field or a method, then if the name starts with an upper-case letter, the declaration is visible outside the current package.
Every Go source file begins with a specification of the package it is defining (possibly only in part). One source file may import declarations from another by specifying the path to the file that contains the declarations, but within the importing code the ex- ported declarations of the imported code are referenced using the imported file’s package name, which need not match that of the imported file’s filename. Of course projects would typically establish standard naming conventions which would align source file names and package names somehow.
Go provides a reflection capability, which is used, for example, to convert an object of an arbitrary type into a human-readable rep- resentation. The “%v” format in Go’s version of printf does this, allowing arbitrarily complex structs to be written out with some- thing as simple as:
Printf is implemented in Go itself, using the “reflect” package.
There are no uninitialized variables in Go. If a variable is not explicitly initialized when declared, it is initialized by default to the zero of its type, where each type has an appropriately-defined zero, typically either the zero numeric value or the nil (aka “null”) pointer value, or some composite object with all components having their appropriate zero value.
What Go leaves out. Because complexity was a major concern during all three of these language designs, some of the most important design decisions were about what to leave out of the language. Here we mention some of the features that Go does not have.
Go does not permit direct cyclic dependencies between packages. However, the Go interface capability permits the construction of recursive control or data structures that cross packages, because an interface declared in one package can be implemented by a type declared in another package without either package being directly dependent on the other.
Like C, Go has no generic template facility. There are some built- in type constructors, such as array, map, and chan, which are effectively parameterized type constructors, but there is no way for the user to create their own such type constructor. Unlike C, there is no macro facility which might be used to create something like a parameterized type. Nevertheless, Go’s flexible interface and reflection capabilities allow the creation of complex data structures that depend only on the presence of a user-provided method such as Hash and the DeepEqual function of the reflect package.
Go does not allow user-defined operators. Various operators are built in for the built-in types, such as int and float32. Interestingly enough, Go does include built-in complex types (complex64 and complex128) with appropriate operators.
Go does not have exceptions. However, functions and methods may return multiple results, and often errors are represented by having a second return value called error that is non-nil on error. Unlike in C, you cannot ignore such an extra parameter; unless you explicitly assign it to an object of name “_”. When things go really wrong in Go, a run-time panic ensues, and presumably during development, you are tossed into a debugger.
Rust from Mozilla Research
The modern web browser represents one of the most complex and critical pieces of software of the internet era. The browser is also a place where performance is critical, and there are many opportunities for using parallelism as a web page is “rendered.” The Rust language arose originally as a personal project by one of the engineers at Mozilla Research (Graydon Hoare), and has grown now into a Mozilla-sponsored research effort. Rust has been designed to help address the complexity of building components of a modern browser-centric software infrastructure, in the context of the new distributed multicore hardware environment.
Like Go, Rust has chosen to simplify storage management by building garbage collection into the language. Unlike Go, Rust has chosen to restrict garbage collection to per-task heaps, and adopt a unique ownership policy for data that can be exchanged between tasks.
What this means is that data that can be shared between tasks is visible to only one of them at a time, and only through a single pointer at a time (hence an owning pointer). This eliminates the possibility of data races between tasks, and eliminates the need for a garbage collector for this global data exchange heap. When an owning pointer is discarded, the storage designated by the pointer may be immediately reclaimed – so no garbage accumulates in the global exchange heap.
Here below is a Rust version of the Word Count program, recursing on multi-character strings with subtasks encapsulated as futures computing the subtotals of each string slice:
Rust does not have special syntax for spawning a “task” (Rust’s equivalent of a “goroutine”) nor declaring the equivalent of a “channel,” but instead relies on its generic template facility and a run-time library of threading and synchronization capabilities. In the above example, we illustrate the use of futures which are essentially a combination of a task and an unbuffered channel used to capture the result of a computation.
There are several other mechanisms for spawning and coordinating tasks, but they all depend on the basic tasking model, as mentioned above, where each task has its own garbage-collected heap for task-local computation (manipulated by what Rust calls managed pointers), plus access via owning pointers to data that can be shared between tasks (by sending an owning pointer in a message).
Rust’s memory management performance. One of the major advantages of the Rust approach to memory management is that garbage collection is local to a single task. By contrast, in Go each garbage collector thread has to operate on data that is potentially visible to all goroutines, requiring a garbage collection algorithm that synchronizes properly with all of the active goroutines, as well as with any other concurrent garbage collector threads (presuming garbage collection itself needs to take advantage of parallel processing to keep up with multi-threaded garbage generation).
In Rust, a conventional single-threaded garbage collector algorithm is adequate, because any given garbage collector is working on a single per-task heap. Furthermore, storage visible via owning pointers needs no garbage collection at all, as releasing an owning pointer means that the associated storage can also be released immediately.
Costs of safety and performance. One of the downsides of added safety and performance can be added complexity. As we see, Rust has added safety by allowing access to sharable data only via pointers that give exclusive access to one task at a time, and added performance because garbage collection is single-threaded. However, as a result, Rust needs several kinds of pointers.
In fact, there are four kinds of pointers in Rust, managed pointers (identified by ‘@’as a prefix on a type) for per-task garbage-collected storage, owning pointers (identified by ‘~’) for data that is sharable between tasks, borrowed pointers (identified by ‘&’) that can temporarily refer to either per-task or sharable data, and raw pointers (identified by ‘*’), analogous to typical C pointers, with no guarantees of safety.