File Name: design and implementation issues of distributed shared memory .zip
But still DSM has some disadvantages and there are lots of important issues which must be handled very carefully in order to successfully implement DSM. Consistency is one such important issue in DSM systems, which must be addressed very carefully by both the computer architects and programmers in order to write correct parallel programs maintaining memory consistency . Shared Memory approach for communication enable simple data sharing through a uniform mechanism of reading and writing shared structures in the common memory.
This system has advantages of ease of programming and portability, although these systems are not scalable. Sometimes it suffers from longer latency for memory accessing, and often the design of memory system is a very complex task [1,3]. Multicomputer Systems i. Message Passing approach for communication are scalable in nature.
Hence, we can obtain high computation power from these systems. Process migration imposes problems because of different address spaces. Therefore, compared to shared-memory systems, hardware problems are easier and software problems more complex in distributed-memory systems [3,13]. DSM Overview:In early days of distributed computing, it was implicitly assumed that programs on machines with no physically shared memory obviously ran in different address spaces. DSM provides a virtual address space shared among different processes in loosely coupled processors.
That is, DSM is basically an abstraction that integrates the local memories of different machines in a network environment into a single logical entity shared by cooperating processes executing on multiple sites. The shared memory itself exists only virtually. Software DSMUntil recently the process communication in distributed systems was limited only to message passing paradigm. But some recent loosely coupled distributed memory systems have implemented a software layer on top of the message passing communication system to provide a shared memory abstraction to the programmers.
The shared memory abstraction gives the system the illusion of physically shared memory and allows the programmers to use the shared memory paradigm. This is the concept of distributed shared memory where the distributed memory is not shared physically but they are shared logically by all the processors. The software layer which is used for providing the shared memory abstraction can be implemented either in an operating system kernel or in runtime library routines with proper system kernel support.
However, hardware implementation requires the addition of special network interfaces and cache coherence circuits to the system to make remote memory access look like local memory access. Cons: Consistency can be an important issue in DSM as different processors access, cache and update a shared single memory space. Thus, remote memory accesses have to be reconciled with the memory manager at each node.
Of course, if the basic machine architecture does not support virtual memory, then the solution could perhaps be simpler. In particular, the local memory at each node may be considered as a large cache memory of the global distributed memory space that spans over the entire network.
GranularityOne of the most important parameters in the design of DSM system is granularity. Granularity refers to the block size or page size of a DSM system. It indicates the unit of data sharing and the unit of data transfer across the network when a block or page fault occurs in the local memory of a site or node.
Selecting proper block size is an important part in designing a good DSM system, otherwise, the system performance degrades. Due to the fact of locality of reference especially spatial locality of reference , it is more likely that the process should refer next to a location, which is a neighbor of the current location. Hence if the page size or block size is greater, then relatively less number of times page faults will occur, which also, in turn, will save the network bandwidth and communication overhead.
However, larger block size also creates some problems. For example, multiple processes at different nodes may refer to the same page or block resulting in the frequent transfer of blocks from one node to another without actually performing the execution task, resulting in a condition called Thrashing.
That is why selecting a suitable block size i. The structure of the shared memory space of DSM system is very much dependent on the applications that the DSM system is intended to support . Memory Coherence and Access SynchronizationIf replication or copy of shared data items is allowed in a DSM system, then maintaining shared data consistency also called memory coherence is an important issue.
This is because multiple sites or nodes may have a copy of the same shared data and if any of them modifies the data then it should be informed to all other nodes, otherwise other processes may use dirty data unknowingly. This problem is similar to multi cache scheme for shared memory multiprocessors. For solving this problem, we need to implement a good memory coherence protocol.
In DSM system, concurrent access to shared data may be allowed. So, memory coherence protocol alone is not sufficient to maintain the consistency of the shared data, we also need some synchronization tools or primitives like semaphore, event flag, lock etc. Therefore, a DSM system must implement some form of data block locating mechanism in order to service network data block faults. Replacement StrategyIn DSM system, the main memory of each node can be considered as a bigger cache of the global shared address space.
So, similar to caching mechanism, if a page fault synonymous to cache fault occurs at a specific site or node when the entire local memory is full, then it causes the replacing of existing page to accommodate the new arrival page. That is data block of local memory must be replaced by new data block. Therefore, a suitable cache replacement strategy e. ThrashingIn a DSM system, data block migrates from one node to another on demand. Therefore, if two nodes compete for write access to a single data item, the corresponding data block may be transferred back and forth at such a high rate that no real wok can get done.
This is called thrashing . DSM system should avoid this situation. HeterogeneityThe DSM system must be designed to take care of heterogeneity so that it functions properly with machines having different architectures.
Memory Coherence. In this section, various consistency models of DSM will be described. Consistency requirements vary from application to application. A consistency model basically refers to the degree of consistency that has to be maintained for the shared memory data for the memory to work correctly for a certain set of applications.
It is defined as the set of rules that applications must follow if they want the DSM system to provide the degree of consistency guaranteed by the consistency model . The memory consistency model of a shared-memory multiprocessor provides a formal specification of how the memory system will appear to the programmer, eliminating the gap between the behavior expected by the programmer and the actual behavior supported by the system.
A consistency model is essentially a contract between the software and the memory. It says that if the software agrees to obey certain rules, the memory promises to work correctly . This model determines the order in which memory operations will appear to execute to the programmer. If we want stronger consistency model, then obviously, the degree of concurrency or parallelism gets down.
So, the basic idea is to invent a consistency model that allows consistency requirements to be relaxed to a higher degree, with the relaxation done in such a way that a set of applications can function correctly. Some major consistency models are described below [1,2, 4].
Strict Consistency ModelThe most stringent consistency model is called strict consistency. It is defined by the following condition: "Any read to a memory location x returns the value stored by the most recent write operation to x. However, in distributed system the existence of absolute global time is not possible and also synchronizing the local clocks of each node perfectly is impossible due to technical limitations.
Hence implementation of strict consistency model in DSM system is practically impossible. To understand this consistency, take an example: In Figure2, consider two processes P1 and P2. The operations done by each process are shown horizontally, with time increasing to the right. Straight lines separate the processes.
The symbols W x a and R y b mean that a write to x with the value a and a read from y returning b have been done, respectively. P1 does a write to location x, storing the value 1. Later, P2 reads x and sees the 1 and this behavior is correct for a strictly consistent memory. A shared memory system is said to be sequential consistent if all the processes see the same order of all memory access operations on the shared memory.
For example, if three operations read r1 , write w1 and read r2 are performed in the same order on a memory address, any of the orderings of the three operations i. If any of the processes see some other ordering of memory access operations than the others, then it is not a sequential consistent memory.
Compared to strict consistency model, sequential consistency model is weaker, because strict consistency model only allows one ordering r1, w1, r2 and nothing else. In a sequentially consistent system, all processors must agree on the order of observed effects.
The following is a legal execution history for SC. Note that R y 2 by processor P3 reads a value that has not been written yet! Of course, this is not possible in any real physical system. However, it shows a surprising flexibility of the Sequential Consistency model. It relaxes the requirements of sequential consistency model for better concurrency. Unlike the sequential consistency model, in this model all processes see only those memory reference operations in the same order that are causally related and the memory reference operations that are not causally related may be seen in different order by different processes.
For example, if a process performs a read operation followed by a write operation on some memory address, then the write operation is causally related to the read operation because the value returned by the read operation must be dependent on the previous write operation.
Obviously, in the implementation of shared memory system supporting this causal consistency model, there is a need to keep track of which memory reference operation is dependent on which other memory reference operations. This can be done by constructing and maintaining a dependency graph for the memory access operation. This is a legal execution history under Causal Consistency but not under Sequential Consistency.
Note that W x 1 and W x 2 are causally related as P2 observed the first write by P1. It only ensures that all write operations performed by a single process are seen by all other processes in the order in which they were performed as if all the write operations performed by a single process are in a pipeline.
In computer science, distributed shared memory DSM is a form of memory architecture where physically separated memories can be addressed as one logically shared address space. Here, the term "shared" does not mean that there is a single centralized memory, but that the address space is "shared" same physical address on two processors refers to the same location in memory. A DSM is essentially a way of seamlessly sharing the physical memories of loosely connected systems. An implementation of a DSM can be Distributed shared memory DSM system is a resource management component of distributed operating system that implements shared memory model in distributed system which have no physically shared memory. The shared memory model provides a virtual address space which is shared by all nodes in a distributed system. The book discusses relevant issues that make the concept of DSM one of the most attractive approaches for building large-scale, high-performance multiprocessor systems. Kshemkalyani and M.
Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. The Designing and Implementing of Distributed Shared Memory Abstract: The distributed shared memory is used to manage the memory in distributed system. In unstructured DSM, The key issues are the granularity of data, transfers, replication of data, memory consistence, and keeping track of the data throughout the system.
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The sea lifted them high in the air and launched them forward upon the rocks. Further, a recent study  shows that an optimized single-thread implementation is able to outperform many distributed systems using many more cores. Our hands-on experi-ments and performance analysis reveal several types of design and implementation de? A hint, betraying nothing overtly. He usually sends one of his researchers first. As an ironclad rule, effectively cutting her off from the other members of the press. With the idea once implanted in their minds there followed a mad panic as the half-brutes fought for places in the remaining boats.
In computer science , distributed shared memory DSM is a form of memory architecture where physically separated memories can be addressed as one logically shared address space. Here, the term "shared" does not mean that there is a single centralized memory, but that the address space is "shared" same physical address on two processors refers to the same location in memory. A distributed-memory system, often called a multicomputer , consists of multiple independent processing nodes with local memory modules which is connected by a general interconnection network.
Distributed shared memory systems provide an easy-to-program parallel environment, to harness the available computing power of PC networks. We present a layered architecture that allows a portable, scalable, and low-cost implementation that runs on Linux and Windows. Only a few, low-level, modules are operating-system dependent; synchronization, distributed memory and consistency management, as well as multithreading are mostly independent. Preliminary results are encouraging; the Linux port performed well, showing high efficiency. Unable to display preview.
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