Книга: Distributed operating systems



In the early days of distributed computing, everyone implicitly assumed that programs on machines with no physically shared memory (i.e., multicomputers) obviously ran in different address spaces. Given this mindset, communication was naturally viewed in terms of message passing between disjoint address spaces, as described above. In 1986, Li proposed a different scheme, now known under the name distributed shared memory (DSM) (Li, 1986; and Li and Hudak, 1989). Briefly summarized, Li and Hudak proposed having a collection of workstations connected by a LAN share a single paged, virtual address space. In the simplest variant, each page is present on exactly one machine. A reference to a local pages is done in hardware, at full memory speed. An attempt to reference a page on a different machine causes a hardware page fault, which traps to the operating system. The operating system then sends a message to the remote machine, which finds the needed page and sends it to the requesting processor. The faulting instruction is then restarted and can now complete.

In essence, this design is similar to traditional virtual memory systems: when a process touches a nonresident page, a trap occurs and the operating system fetches the page and maps it in. The difference here is that instead of getting the page from the disk, the operating system gets it from another processor over the network. To the user processes, however, the system looks very much like a traditional multiprocessor, with multiple processes free to read and write the shared memory at will. All communication and synchronization can be done via the memory, with no communication visible to the user processes. In effect, Li and Hudak devised a system that is both easy to program (logically shared memory) and easy to build (no physically shared memory).

Unfortunately, there is no such thing as a free lunch. While this system is indeed easy to program and easy to build, for many applications it exhibits poor performance, as pages are hurled back and forth across the network. This behavior is analogous to thrashing in single-processor virtual memory systems. In recent years, making these distributed shared memory systems more efficient has been an area of intense research, with numerous new techniques discovered. All of these have the goal of minimizing the network traffic and reducing the latency between the moment a memory request is made and the moment it is satisfied.

One approach is not to share the entire address space, only a selected portion of it, namely just those variables or data structures that need to be used by more than one process. In this model, one does not think of each machine as having direct access to an ordinary memory but rather, to a collection of shared variables, giving a higher level of abstraction. Not only does this strategy greatly reduce the amount of data that must be shared, but in most cases, considerable information about the shared data is available, such as their types, which can help optimize the implementation.

One possible optimization is to replicate the shared variables on multiple machines. By sharing replicated variables instead of entire pages, the problem of simulating a multiprocessor has been reduced to that of how to keep multiple copies of a set of typed data structures consistent. Potentially, reads can be done locally without any network traffic, and writes can be done using a multicopy update protocol. Such protocols are widely used in distributed data base systems, so ideas from that field may be of use.

Going still further in the direction of structuring the address space, instead of just sharing variables we could share encapsulated data types, often called objects. These differ from shared variables in that each object has not only some data, but also procedures, called methods, that act on the data. programs may only manipulate an object's data by invoking its methods. Direct access to the data is not permitted. By restricting access in this way, various new optimizations become possible.

Doing everything in software has a different set of advantages and disadvantages from using the paging hardware. In general, it tends to put more restrictions on the programmer but may achieve better performance. Many of these restrictions (e.g., working with objects) are considered good software engineering practice and are desirable in their own right. We will come back to this subject later.

Before getting into distributed shared memory in more detail, we must first take a few steps backward to see what shared memory really is and how shared-memory multiprocessors actually work. After that we will examine the semantics of sharing, since they are surprisingly subtle. Finally, we will come back to the design of distributed shared memory systems. Because distributed shared memory can be intimately related to computer architecture, operating systems, runtime systems, and even programming languages, all of these topics will come into play in this chapter.

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