In computer science and engineering, transactional memory attempts to simplify concurrent programming by allowing a group of load and store instructions to execute in an atomic way. It is a concurrency control mechanism analogous to database transactions for controlling access to shared memory in concurrent computing.
The motivation of transactional memory lies in the programming interface of parallel programs. The goal of a transactional memory system is to transparently support the definition of regions of code that are considered a transaction, that is, that have atomicity, consistency and isolation requirements. Transactional memory allows writing code like this example:
def transfer_money(from_account, to_account, amount): with transaction(): from_account -= amount to_account += amount
In the code, the block defined by "transaction" has the atomicity, consistency and isolation guarantees and the underlying transactional memory implementation must assure those guarantees transparently.
Hardware vs. software
Hardware transactional memory systems may comprise modifications in processors, cache and bus protocol to support transactions. Load-link/store-conditional (LL/SC) offered by many RISC processors can be viewed as the most basic transactional memory support. However, LL/SC usually operates on data that is the size of a native machine word, so only nano-transactions are supported.
Software transactional memory provides transactional memory semantics in a software runtime library or the programming language, and requires minimal hardware support (typically an atomic compare and swap operation, or equivalent). As the downside, software implementations usually come with a performance penalty, when compared to hardware solutions.
Owing to the more limited nature of hardware transactional memory (in current implementations), software using it may require fairly extensive tuning to fully benefit from it. For example, the dynamic memory allocator may have a significant influence on performance and likewise structure padding may affect performance (owing to cache alignment and false sharing issues); in the context of a virtual machine, various background threads may cause unexpected transaction aborts.
One of the earliest implementations of transactional memory was the gated store buffer used in Transmeta's Crusoe and Efficeon processors. However, this was only used to facilitate speculative optimizations for binary translation, rather than any form of speculative multithreading, or exposing it directly to programmers. Azul Systems also implemented hardware transactional memory to accelerate their Java appliances, but this was similarly hidden from outsiders.
Sun Microsystems implemented hardware transactional memory and a limited form of speculative multithreading in its high-end Rock processor. This implementation proved that it could be used for lock elision and more complex hybrid transactional memory systems, where transactions are handled with a combination of hardware and software. The Rock processor was cancelled by Oracle in 2009; while the actual products were never released, a number of prototype systems were available to researchers.
In 2009, AMD proposed the Advanced Synchronization Facility (ASF), a set of x86 extensions that provide a very limited form of hardware transactional memory support. The goal was to provide hardware primitives that could be used for higher-level synchronization, such as software transactional memory or lock-free algorithms. However, AMD has not announced whether ASF will be used in products, and if so, in what timeframe.
More recently, IBM announced in 2011 that Blue Gene/Q had hardware support for both transactional memory and speculative multithreading. The transactional memory could be configured in two modes; the first is an unordered and single-version mode, where a write from one transaction causes a conflict with any transactions reading the same memory address. The second mode is for speculative multithreading, providing an ordered, multi-versioned transactional memory. Speculative threads can have different versions of the same memory address, and hardware implementation keeps tracks of the age for each thread. The younger threads can access data from older threads (but not the other way around), and writes to the same address are based on the thread order. In some cases, dependencies between threads can cause the younger versions to abort.
The most recent development is Intel's Transactional Synchronization Extensions (TSX) and the actual implementation in Haswell processors. Haswell is the first x86 processor to feature hardware transactional memory. Intel's TSX specification describes how the transactional memory is exposed to programmers, but withholds details on the actual transactional memory implementation.
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