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January 12, 2007
Getting Serious About Transactional Memory
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The parallelization of computing, via multi-threading cores, multi-core processors and multi-processor systems is encouraging ever greater levels of application concurrency to take advantage of the proliferating CPUs. Multi-core processors, in particular, are fueling this phenomenon.
Beyond the dual- or quad-core domain, manufacturers are starting to build many-core chips. Examples include the Niagara T1 UltraSPARC from Sun Microsystems, which has 8 cores and can support 32 threads (the next-generation Rock processor will double this to 16 cores and 64 threads); Cavium Networks 16-core OCTEON MIPS64 processor for embedded applications; and Intel's Polaris prototype processor, which sports 80 cores and boasts a peak teraflop.
The Polaris prototype, which is part of Intel's terascale computing initiative, is motivating researchers there to take a hard look at a relatively new technology -- transactional memory or TM, for short. Within the next ten years, the prospect of multi-core teraflop processors like Polaris and new application domains to use those processors will require vastly more parallel processing than ever before. Even incorporating relatively low levels of concurrency in today's applications is already challenging some of our best developers.
One of the nastiest concurrency problems has to do with keeping data thread-safe, that is maintaining global data integrity in the presence of parallel executing threads. Failure to keep data thread-safe leads to deadlocks, race conditions (data corruption) and priority inversion. Worse, because these types of problems are time-sensitive, they are often very hard to find during normal testing and in some cases go undetected until after the application is deployed.
The typical way to keep data thread-safe is to use global locks around objects that are being accessed by more than one thread. Locks provide a synchronization mechanism that blocks concurrent access of an object, preventing the data race condition. Seems simple enough. But there are a number of problems with this approach. Sometimes locks become dependent on each other, such that each thread is holding a lock the other thread needs. Or if a thread dies holding a lock it can block other dependent threads.
Even for correctly implemented locks, there's the issue of granularity. Coarse granularity protects larger data objects and uses fewer locks. But as the number of threads scales up, performance suffers. Finer granularity allows the programmer to protect smaller data items and gives better performance as long (as lock overhead is not overwhelming). It makes it possible, for example, to lock individual record components rather than the entire record structure. But finer granularity requires more complex algorithms and more locks, so it is often much more difficult to implement correctly.
Transactional memory to the rescue
Terascale computing, which relies on many-core parallelism, will be very difficult to develop. The current languages only provide low-level concurrency features. For Intel, terascale computing has become the prime motivator to improve software concurrency technology. The company's 80-core Polaris prototype will require much greater levels of application concurrency than today. And the scaling up of multi-core processors across time and product families will necessitate a solution that doesn't require reprogramming based on core count.
"How can the programmer write parallel code more effectively, that is, write robust code that doesn't have bugs, but still scales and benefits from the additional cores that each successive generation provides," asks Ali-Reza Adl-Tabatabai, Intel Principal Engineer? "That's the big challenge that we're going after."
To that end, Intel researchers are looking to transactional memory as one of the key technologies that will enable developers to write the terascale killer apps of the next decade. The attraction of TM is that is appears to solve the most annoying problems of global locks: application robustness and scalability. These attributes are especially important for the type of large-scale concurrency required by terascale applications.
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