ECE506 CSC/ECE 506 Spring 2013/11a ad
Introduction
When dealing with a relatively small number of processors (8-16), according to Solihin 320, using a bus based shared memory structure is fine. Unfortunately, when you need to provide a shared memory structure for processors much greater than that, you will need a different set of organization. This new organization is needed due to the physical limitations of the bus. There are two ways you can create such a system. These include Distributed Shared Memory (DSM) or Non-Uniform Memory Access (NUMA). The benefits of having a DSM and NUMA is that we can now scale to a larger amount of processors. The disadvantage is that scaling in such a way may not be the most cost-effective solution, Solihin 320. For the remainder of this section, we will be discussing the performance of DSM's.
According to Solihin 320, there are two aspects that restrict the scalability of bus-based multiprocessors. These include the physical limitations of interconnections and the limitations of the protocol. To explain in detail, on a bus-based system, adding a processor will not affect any other physical restrictions on the system. Unfortunately, when adding a new processor, you will be reducing the speed of the bus. Second, the protocol needed to keep coherence does not scale well. As you increase the number of processors to the system, the amount of traffic also increases. This means that you might run the risk of overwhelming the bandwith. According to Solihin, there are a few ways that we can mitigate this problem. The following is from 321 of the Solihin textbook.
From the table, we can see that there is three ways to scale a multiprocessor system. The first being a single bus system. This is the least scalable due to the limitations of the bus wire itself. As you add processors you will decrease the bus speed due to having to increase the wire length. Also, you run into an issue of overwhelming the bus due to the amount of traffic. The second way is to use a point-to-point bus system. This allows for the speed of the bus to remain relatively fast, but since the traffic will also scale with the number of processors, there will be a limitation due to overwhelming the bus system with traffic. Lastly, the most scalable system to date is using a directory system. This allows for the bus to remain fast due to the short wires, and the bus traffic to remain low since the directory holds information on cache locations.
To provide an overview
of DSM, all possible platforms must be considered in DSM design.The choice relies on classifying all existing systems into appropriate non-overlapping subsets of systems.
DSM implementation level types-
1 Hardware
2 Software
2.1 Operating system
2.1.1 Inside the kernel
2.1.2 Outside the kernel
2.2 Runtime library routines
2.3 Compiler-inserted primitives
3 Hardware/software combination
The level of DSM implementation affects both the programming model and the overall system performance. While the
hardware solutions bring total transparency to the programmer, and achieve very low access latencies, software solutions can better exploit the application behavior and represent the
ideal polygon to experiment with new concepts and algorithms.
As the consequence, the number of software DSM systems presented in the open literature is considerably higher, but the
systems intending to become commercial products and standards are mostly hardware-oriented.Architectural configuration of the system affects the system performance, since it can offer or restrict a good potential for parallel processing of requests related to the DSM management. It also strongly affects the scalability. Since a system applying a DSM mechanism is usually organized as a set of clusters
connected by an interconnection network, architectural parameters include:
a) Cluster configuration (single/multiple processors, with/without, shared/private, single/multiple level caches, etc.) b) Interconnection network (bus hierarchy, ring, mesh, hyper- cube, specific LAN, etc.)
Cluster configuration is usually very important for the hardware-oriented proposals that integrate the mechanisms of cache
coherence on the lower level with the DSM mechanisms on the higher level of the system organization, or even store all shared data in large caches. Cluster configuration is mostly transparent for software solutions, It includes the memory organization and
the placement of directory, as well.
Almost all types of interconnection networks found in multiprocessors and distributed systems have also been used in DSM systems, The majority of software-oriented DSM systems were actually build on the top of Ethernet, although some of the solutions tend to be architecture independent and portable to various platforms. On the other hand, topologies such as bus hierarchy or mesh are typical for hardware solutions. The choice of topology can be also very important for the implementation of DSM algorithm, since it affects the possibility and cost of broad-
cast and multicast transactions.
The impact of organization to the overall system performance is closely related to the locality of data access.
Hardware solutions always deal with non-structured data objects (typically cache blocks), while many software implementations tend to use data items that represent logical entities, in order to take advantage of the locality naturally expressed by the application. On the other hand, some software solutions, based on virtual memory mechanisms, organize data in larger physical
blocks (pages), counting on the coarse-grain sharing.
Software DSM<ref>http://csag.ucsd.edu/individual/yskee/publication/parco04.pdf</ref>
Software distributed shared memory (SDSM) systems which provide shared address space have been of great importance to distributed memory architectures.
Early SDSM systems like IVY [1], Midway [2], Munin [3], and TreadMarks [4] assume uniprocessor nodes, thus allow only one thread per process on a node. Currently, commodity off-the-shelf microprocessors and network components are widely used as building blocks for parallel computers. This trend has made cluster systems consisting of symmetric multiprocessors (SMPs) attractive platforms for high performance computing. However, the early single-threaded SDSM systems are too restricted to exploit multiprocessors in SMP clusters. The next generation SDSM systems like Quarks [5], Brazos [6], DSM-Threads [7], and Murks [8] are aware of multiprocessors and exploit them by means of multiprocesses or multithreads. In general, naive process-based systems experience high context switchingoverhead and additional inter-process communication delay within a node, so the focus is on multi-threaded SDSM systems. Many single-threaded SDSM systems are implemented at user-level by using the
page fault handling mechanisms.
The SDSM system faces a dilemma when multiple threads compete to access an invalid
page within a short interval. On the first access to an invalid page, the system should set the page writable to replace with a valid one. Unfortunately, this change also allows other application threads to access the same page freely. This phenomenon is known as atomic page update and change right problem [7] or a race condition [8]. A known solution to this problem adopted by major multithreaded SDSM systems like TreadMarks [9], Brazos [6], and Strings [10] is to map a file to two different virtual addresses. Even though the file mapping method achieves good performance on some systems, file mapping is not always the best solution. Operating system and working environment may severely affect the performance of these systems. The file mapping method performs poorly in some cases; for example, an IBM SP Night Hawk system with AIX 4.3.3 PSSP 3.2 version .Moreover, file mapping has high initialization cost and reduces the available address space because SDSM and application partition the address space. We note the cause of the atomic page update problem is that SDSM and application share the same address space. When SDSM changes a page writable, the page is also accessible to the application. A general solution to this problem is to separate the application address space from the system address space for the same physical memory, and to assign different access permission to each address space.
These systems implement the shared memory abstraction completely in software on top of message-passing hardware. In terms of performance, software DSMs often exhibit high remote data access latencies when running real parallel applications. Relaxed consistency and multiple-writer coherence are common approaches to mitigating these latencies. Unfortunately, these techniques have not been enough to significantly broaden the class of applications for which software DSMs are efficient. As a result, several other techniques have been proposed to address these high latencies. The best-known of these techniques are prefetching and coherence protocol
adaptation to sharing patterns.<ref>http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=01247677</ref>
Hardware Support<ref>http://www.csl.cornell.edu/~espeight/papers/CSL-TR-2000-1008.pdf</ref>
Scalable cache-coherent distributed shared-memory (DSM) machines have received much attention in the literature since the late 1980s. To demonstrate their effectiveness, several cache-coherent non-uniform memory access (CC-NUMA) hardware DSM machines were built in the research com- munity (e.g. DASH [26], Alewife [2], FLASH [22], Typhoon [36]) and commercial machines followed (e.g. SGI Origin 2000 [23], Sun S3.mp [33], Sequent NUMA-Q [29], HP Exemplar [1], Data General Aviion [7]). At the same time, a large research effort produced a set of scientific benchmarks with which to evaluate DSM machines [48]. Most high-performance hardware DSM machines have tightly-integrated node or memory controllers that connect the microprocessor both to the memory system and to a proprietary high-speed switching network. The scalable coherence protocols (e.g., [6,14,27,38,41,42]) used in such machines are implemented either in hardware finite-state machines or in software running on an embedded programmable device in the controller. Despite the resulting high performance of these systems, and efforts to show that the necessary additional hardware to support hardware DSM in commodity workstations and servers is small [25], high-end PC servers and engineering workstations have yet to integrate the additional functionality needed to build seamless hardware DSM from COTS
(commodity of-the-shelf ) components.
Performance Concerns
A DSM machine has unique requirements compared to shared memory / bus based machines in order to provide cache coherence and memory consistency, as well as having interconnects. Performance concerns of each of these concepts in DSM machines are discussed in detail.
Cache coherence
The presence of multiple cached copies of a page requires a mechanism to notify other sharers
of a modified memory location. A new value is propagated by either invalidating or updating each copy, which correspond to write-invalidate and write-update coherence protocol respectively. Generally, the coherence protocol consists of the data structures and algorithms used
to implement a given memory consistency model.
A cache coherence protocol is in fact a mechanism of propagating newly written values so that all processors have a coherent view of the shared memory. It is normally designed under the requirement of a memory consistency model which specifies the coherence requirement on a cache coherence protocol, i.e., what “coherent view” of shared memory a cache coherence protocol should provide. Many new ideas accompany with relaxed memory consistency models have been proposed to efficiently propagate newly written values. Examples of these new ideas
include lazy release consistency, and delay consistency etc.
Traditionally, there are two main methods to maintain coherence: snoopy protocol and directory-based protocol. The snoopy coherence protocol requires the support of hardware so it is not widely used in software DSM systems. Up to now, almost all software DSM systems adopt the directory scheme or that similar to directory scheme. However, the scalability of the directory-based scheme is limited by the organization mode of the directory. Therefore, we
propose a new scheme to maintain the coherence between multiple cache copies.
Memory consistency <ref>http://www.cs.wayne.edu/~weisong/papers/shi00-jiajia.pdf</ref>
The Memory consistency model is an interface between the programmer and the system.
The memory consistency model of a shared memory system formally specifies how the memory system will appear to the programmer. Normally, a memory consistency model defines constraints on the order in which memory accesses can be performed in shared memory systems. It influences both programming complexity and performance. The stricter the memory consistency model, the easier for programmer to program, and the less opportunity for optimization. A strict consistency model like sequential consistency is intuitive to the programmer. However, with the large granularity of coherence unit (a page) in shared virtual memory systems, the false sharing problem will be so serious that the performance of software DSM systems under sequential consistency is very poor. For example, the performance of the first software DSM system IVY is so poor that the main contribution of it is the original idea about software DSM, while the practical system is useless. To improve performance, software DSM systems normally adopt relaxed memory consistency models which separate synchronization operations from ordinary load and store operations and allow ordinary operations to be executed out of order. The propagation and application of coherence operations are postponed until synchronization points. In the past decade, almost 20 different memory consistency models have been proposed for hardware-coherent systems. Among those relaxed consistency models, release consistency which separates acquire from release synchronization inspire a major breakthrough in the performance of software DSM
systems.
Although the memory consistency model specifies when coherence operations and data need to become visible, it can actually be implemented with various degrees of “laziness” in the propagation and application of both coherence and data. Greater laziness implies greater complexity and protocol and state, but fewer communication and protocol operations. For example, hardware-coherent systems that use release consistency tend to propagate coherence and apply them immediately, thus simplifying the data structures that need to memorize the
state of the cache line.
In software DSMs, it is very important to reduce the number of messages exchanged, because
sending a message in a software DSM is more expensive than that in a hardware DSM.
The TreadMarks’ lazy implementation of release consistency goes further. It does not propagate the modifications in a critical section at the time of release. Instead, modifications are buffered and are propagated merely to the processor that acquires the released lock until the time of acquire. In this way, lazy release consistency reduces both the number of messages and the amount of data exchanged. In LRC, before a processor can pass an acquire operation, all modifications that have been visible to the releasing processor must also be visible to the
acquiring processor.
Latency of interconnections
A mentioned in the cache coherency and memory consistency sections, interconnections unique distinguish DSM systems from bus based systems. Interconnections are unlike a bus in that they do not guarantee that messages reach recipients, and certainly aren't seen by the receivers at the same moment. Each message must be sent as a transaction in a networking protocol, and each packet sent has at least the latency of hops through routers in the network, and can also incur latency in being generated.
Since messages can become ubiquitous if the DSM system was naively designed to perform identically to a bus based system, care must be taken to design coherence protocols and consistency models that minimize the sending and receiving of messages, and if they must be sent or received, allow for overlapping of execution without blocking while waiting for messages or their receipt.
Granularity<ref>http://www.cdk5.net/dsm/Ed4/Chapter%2018%20DSM.pdf</ref>
An issue that is related to the structure of DSM is the granularity of sharing.
Conceptually, all processes share the entire contents of a DSM. As programs sharing DSM execute, however, only certain parts of the data are actually shared and then only for certain times during the execution. It would clearly be very wasteful for the DSM implementation always to transmit the entire contents of DSM as processes access and update it. What should be the unit of sharing in a DSM implementation? That is, when a process has written to DSM, which data does the DSM runtime send in order to provide
consistent values elsewhere?
The focus here is on page-based implementations, although the granularity issue
does arise in other implementations. In a page-based DSM, the hardware supports alterations to an address space efficiently in units of pages – essentially by the placement of a new page frame pointer in the page table (see, for example, Bacon [2002] for a description of paging). Page sizes can typically range up to 8 kilobytes, so this is an appreciable amount of data that must be transmitted over a network to keep remote copies consistent when an update occurs. By default, the price of the whole page transfer must be paid whether the entire page has been updated, or just
one byte of it.
Using a smaller page size does not necessarily lead
to an improvement in overall performance. First, in cases where processes do update large amounts of contiguous data, it is better to send one large page rather than several smaller pages in separate updates, because of the fixed software overheads per network packet. Second, using a small page as the unit of distribution leads to a large number of units that must be administered separately by the DSM implementation. To complicate matters further, processes tend to contend more for pages when the page size is large, because the likelihood that the data they access will lie within the same page increases with the page size. Consider, for example, two processes, one of which accesses only data item A while the other accesses only data item B, which lie within the same page. For the sake of concreteness, let us assume that one process reads A and the other updates B. There is no contention at the application level. However, the entire page must be transmitted between the processes, since the DSM runtime does not by default know which locations in the page have been altered. This phenomenon is known as false sharing: two or more processes share parts of a page, but only one in fact accesses each part. In write-invalidate protocols, false sharing can lead to unnecessary invalidations. In write-update protocols, when several writers falsely share data items they may cause them to be overwritten with older versions. In practice, the choice of the unit of sharing has to be made based on the physical page sizes available, although a unit of several contiguous pages may be taken if the page size is small. The layout of data with respect to page boundaries is an important factor in determining the number of page transfers made when a program executes.
Thrashing
A potential problem with write-invalidate protocols is thrashing. Thrashing is said to occur where the DSM runtime spends an inordinate amount of time invalidating and transferring shared data compared with the time spent by application processes doing useful work. It occurs when several processes compete for the same data item, or for falsely shared data items. If, for example, one process repeatedly reads a data item that another is regularly updating, then this item will be constantly transferred from the writer and invalidated at the reader. This is an example of a sharing pattern for which write-invalidate is inappropriate and write-update would be better. The next section describes the Mirage approach to thrashing, in which computers ‘own’ pages for a minimum period; Section 18.4 describes how Munin allows the programmer to declare access patterns to the DSM system so that it can choose appropriate update options for each data item and avoid thrashing.
Saclabilty<ref>http://www.cdf.toronto.edu/~csc469h/fall/handouts/nitzberg91.pdf</ref>
A theoretical benefit of
DSM systems is that they scale better than tightly coupled shared-memory multiprocessors. The limits of scalability are greatly reduced by two factors: central bottlenecks (such as the bus of a tightly coupled shared- memory multiprocessor), and global common knowledge operations and storage (such as broadcast messages or full directories, whose sizes are proportional to the number of nodes). Li and Hudak2 went through several iterations to refine a coherence protocol for Ivy before arriving at their dy- namic distributed-manager algorithm,
which avoids centralized bottlenecks.
However, Ivy and most other DSM
systems are currently implemented on top of Ethernet (itself a centralized bottleneck), which can support only about 100 nodes at a time. This limitation is most likely a result of these systems being research tools rather than an indication of any real design flaw. ShivaY is an implementation of DSM on an Intel iPSCR hypercube, and it should scale nicely. Nodes in the Dash system are connected on two meshes. This implies that the machine should be expandable, but the Dash prototype is currently limited by its use of a full bit vector (one bit per node) to keep track of page replication. Heterogeneity. At first glance, sharing memory between two machines with different architectures seems almost impossible. The machines may not even use the same representation for basic data types (integers, floating-point numbers, and so on). It is a bit easier if the DSM system is structured as variables or objects in the source language. Then a DSM compiler can add conversion routines to all accesses to shared memory. In Agora, memory is structured as objects shared among heterogeneous machines. Mermaidlo explores another novel approach: Memory is shared in pages, and a page can contain only one type of data. Whenever a page is moved between two architecturally different systems, a conversion routine converts the data in the page to the appropriate
format.
Although heterogeneous DSM might allow more machines to participate in a computation, the overhead of conversion seems to outweigh the benefits. Related algorithms. To support a DSM system, synchronization operations and memory management must be specially tuned. Semaphores, for example, are typically implemented on shared- memory systems by using spin locks. In a DSM system, a spin lock can easily cause thrashing, because multiple nodes may heavily access shared data. For better performance, some systems pro- vide specialized synchronization primitives along with DSM. Clouds provides semaphore operations by grouping semaphores into centrally managed segments. Munin supports the synchroni- zation memory type with distributed locks. Plus supplies a variety of syn- chronization instructions, and supports delayed execution, in which the syn- chronization can be initiated, then later tested for successful completion. Dubois, Scheurich, and Briggs12 discuss the rela- tionship between coherence and synchronization. Memory management can be restructured for DSM. A typical memory- allocation scheme (as in the C library malloc()) allocates memory out of a common pool, which is searched each time a request is made. A linear search of all shared memory can be expensive. A better approach is to partition avail- able memory into private buffers on each node and allocate memory from the global buffer space only when the
private buffer is empty.
Other Factors
Application Programming Interface
As we know, whether a new software system is competitive depends greatly on the friendship
of it’s application programming interface(API). In parallel computing field, shared memory and message passing are two mainstream programming model which are widely used in the world. Shared memory programming model is a natural extension of traditional sequential programming model so that it is easy to be accepted by the application programmers. In this model, the application programmer does not need to consider the data partition, migration and underlying communication mechanism. On the contrary, the message passing programming model requires the application programmer to take the data partition into account, and use system supplied communication functions explicitly, which burden the application programmer much. As a result, the shared memory model is widely adopted and advocated by the researchers, application programmers and corporations, which fuels the success of shared virtual memory
system.
Generally speaking, the programming model of software DSM system is similar to traditional shared memory systems, such as Single Program and Multiple Data (SPMD) and Multithreading, which are familiar with many programmers. However, the API of a software DSM system is closely related to the memory consistency model implemented in the system and the implementation level of the system. For example, in a software DSM system with sequential consistency, the programmer can write the program just like a general SPMD programming mode. They can allocate a shared variable at any time and use them easily. However, if the Entry Consistency is used, the application programmer must mark the affinity between
shared data and synchronization object explicitly in the program.
The implement level of software DSM system will affect the programming interface too. If the system is implemented on the language level, such as Linda and Orca, the programmer must learn those new characteristics in the languages related to shared memory address space. If the system is implement on the complier or operating system level, all the work are done by the compiler and all the changes are transparent to application users, as in Shasta[100]. If the software DSM system is implemented by runtime library, what the application programmer should do is adding some function calls in the source program, and linking the related library
when compiling.
Memory Organization
The memory organization of a software DSM system determines the way shared virtual memory
is organized on top of the isolated memory address space. It has great influence on the cache
coherence protocol and consequently on system overhead.
Normally, the shared virtual memory address space of a software DSM system is organized in a COMA-like way, where the local memory of a host is treated as a large cache, and pages can be replicated and migrated on demand. Migrating the owner of data according to the sharing pattern reduces the probability of access miss for some applications characterized with single writer reference pattern, but requires a mechanism to locate the data when local access is failed. In systems that adopt the COMA shared virtual memory organization scheme, each page has an owner, and mechanisms such as probable owner or approximate copyset are employed to find where the owner of the faulting page is when the page fault occurs. Examples of this kind
of system includes IVY, Munin, TreadMarks, CVM, and Quarks.
The shared virtual address space of a software DSM can also be organized like traditional CC-NUMA. In this kind of system, each shared page has a fixed home, when the page faulting occurs, the faulting processor can fetch the up-to-date page from the home directly, only one round-trip communication is needed. This requires that the coherence protocol to propagate modified information to the home node on time. The main advantage of the home-based protocol is its very low memory overhead compared to homeless counterpart. Besides the simplicity of servicing ordinary page fault, another advantage of the home-based software DSM system is that no diffs generation are required for writes to home pages. Evaluation results from Zhou demonstrate that home-based system have comparable or better performance than its COMA counterparts, though platform dependent evaluation make the evaluation results not so convincible. Some other memory organization schemes which stem from COMA and NUMA have been implemented and studied in DSM systems. The I-ACOMA and simple-COMA are examples of these varieties.
References
<references/>