To achieve the highest performance, we employ a combination of thread binding, numaaware thread allocation, and relaxed global coordination among threads. Numaaware scheduling and memory allocation for data. In this situation, the reference to the article is placed in what the author thinks is the. We evaluate our locks in both kernel space and in userspace, and find that our lock algorithms. Under numa, a processor can access its own local memory faster than nonlocal memory memory local to another processor or memory shared between processors. Userlevel scheduling on numa multicore systems under linux.
In order to achieve locality of memory access on a numa architecture, salsa chunks are kept in the. The principle of keeping numa local data structures was previously used by dice et al. To prove our point, we focus on a primitive that is used as the. Introduction graph partitioning is used extensively to orchestrate parallel execution of graph processing in distributed systems 1, diskbased processing 2, 3 and numaaware shared memory systems 4, 5. The optimization is based on nonuniform memory access balanced task and loop parallelism numabtlp algorithm stirb. Experimental results show that our numaaware virtual ma. Our rw lock algorithms build on the recently developed lock cohorting technique 7, which allows for the construction of numa aware mutual exclusion locks. A brief survey of numa nonuniform memory architecture. All the designs and optimizations are generic, independent of the graph algorithms, and are part of our numa aware graph processing.
All the algorithms discussed in the following subsections are numaaware to reduce intersocket memory tra c. Numaaware concurrency control for scalable transactional memory icpp 2018, august 16, 2018, eugene, or, usa the second experiment shows the latency needed for incrementing a logically shared timestamp via a compareandswap cas primitive. Numa aware locks that is as simple as it is powerful. However, to the best of our knowledge, there has been no prior effort toward constructing numaaware rw locks. The end result is a collection of surprisingly simple numa aware algorithms that outperform the stateoftheart readerwriter locks by up to a factor of 10 in our microbenchmark experiments. While allocating memory in a serial region and faulting it in a parallel region will usually impart the right affinity, the safest method is to allocate data private to. Our runtime algorithms for numa aware task and data placement are fully automatic, applicationindependent, performanceportable across numa machines, and adapt to dynamic changes.
The multicore evolution has stimulated renewed interests in scaling up applications on sharedmemory multiprocessors, significantly improving the scalability of many applications. Red hat enterprise linux numa support for hpe proliant. Numa nonuniform memory access is the phenomenon that memory at various points in the address space of a processor have different performance characteristics. Their paper presents an extension of their openmp runtime system that connects a thread scheduler to a numa aware memory management system, which they choose to call forestgomp. Section 7 puts it altogether and introduces graphite. Numa aware io in virtualized systems rishi mehta, zach shen, amitabha banerjee. To reduce capacity cache misses for largevector reductions, we use strip mining for large vectors in all algorithms. Extending numabtlp algorithm with thread mapping based on a. Similarly to their work, our algorithms data allocation scheme is designed to reduce interchip communication. Numaaware datatransfer measurements for powernvlink multigpu systems.
This property inspired many numaaware algorithms for operating systems. Numaaware graph mining techniques for performance and. Numaaware scheduling and memory allocation for data flow. Numaaware mutex locks have been explored in depth 3, 7, 17, 19. Pdf improving parallel system performance with a numaaware. Contents preface xiii i foundations introduction 3 1 the role of algorithms in computing 5 1. A numaaware clustering library capable of operating. The principle of keeping numalocal data structures was previously used by dice et al.
X kernels and now includes a significant subset of the numa features expected in an enterpriseclass operating system. This paper revisits the design and implementation of distributed shared. As far as i can tell the drop in performance in such algorithms is caused by nonlocal memory access from a second numanode. The linux kernel gained support for cachecoherent nonuniform memory access numa systems in the linux 2. The paper presents a nonuniform memory access numaaware compiler optimization for tasklevel parallel code. To achieve the highest performance, we employ a combination of thread binding, numa aware thread allocation, and relaxed global coordination among threads. Second, inspired by vertexcuts 22 from distributed graph sys.
Evolutionary programming an overview sciencedirect topics. With this technique, large vectors are divided into chunks so that each chunk can t into the lastlevel cache. The end result is a collection of surprisingly simple numaaware algorithms that outperform the stateoftheart readerwriter locks by up to a factor of 10 in our microbenchmark experiments. But the scalability is limited within a single node. So the question is how to make the application perform better.
We also implement a readerswriter lock on top of our blocking lock. Intel 64 and ia32 architectures software developers manual. The algorithm is based on a pareto ranking scheme, i. Existing stateoftheart contention aware algorithms work as follows on numa systems. Related work graph algorithms are traditionally solved over centralized and distributed graph analytics systems, which pur. Adaptive numaaware data placement and task scheduling for. Scalable and low synchronization numaaware algorithm for producerconsumer pools elad gidron cs department technion, haifa, israel. In this paper, we propose a numaaware thread and resource scheduling for optimized data transfer in terabit network. The document is divided into categories corresponding to the type of article being referenced. A case for numaaware contention management on multicore systems. Associating the shared data allocation with each thread in a numaaware fashion is much more complicated. Accelerating bfs and sssp on a numa machine for the. Moreover, these solutions are not applicable when applications compose data structures and wish to modify several of them with a single composed operation e. Briefly, nr implements a numaaware shared log, and then uses the log to replicate data structures consistently across numa nodes.
Morseldriven query processing takes small fragments of input data morsels and schedules these to worker. Furthermore, our use of pagesize chunks allows for data migration in numa architectures to improve locality, as done in 8. This encourages the design of numaaware algorithms 21, 22, 5, 23 that minimize remotenode memory accesses. We further add a core oversubscription policy to implement a blocking lock. To optimize performance, linux provides automated management of memory, processor, and io resources on most numa systems. Numa oblivious data partitioning vs numa aware data partitioning. A userlevel numaaware scheduler for optimizing virtual. Existing stateoftheart contentionaware algorithms work as follows on numa systems. Numa aware io in virtualized systems hot interconnects.
Extending numabtlp algorithm with thread mapping based. Lock cohorting allows one to transform any spinlock algorithm, with minimal nonintrusive changes, into a scalable numa aware spinlock. First, being aware of the hierarchical parallelism and locality in numa machines, polymer is extended with a hierarchical scheduler to reduce the cost for synchronizing threads on multiple numanodes. The numa aware scheduling algorithm properly selects a best numa node for a vm. This property inspired many numa aware algorithms for operating systems. Numa aware multicore blas matrix multiplication 3 implements mesif modi ed, exclusive, shared, invalid, forward protocol in intel nehalem a processors to maintain the data coherency 2 3. This document presents a list of articles on numa nonuniform memory architecture that the author considers particularly useful. Numaaware mutex lock designs pursue only one goal reduction of the lock 158. In recent years, a new breed of nonuniform memory access numa systems has emerged.
Virtually all those locks, however, are hierarchical in their nature, thus requiring space proportional to the number of sockets. Numa awareness affects all layers of the library i. Lock cohorting allows one to transform any spinlock algorithm, with minimal nonintrusive changes, into a scalable numaaware spinlock. Numa aware locks that exploit this behavior by keeping the lock ownership on the same socket, thus reducing remote cache misses and intersocket communication. We show that using numaaware task and data placement, it is possible to preserve the uniform abstraction of both computing and memory resources for taskparallel programming models while achieving high data locality. The importance of such numaaware algorithm designs will only increase, as future server systems are expected to feature ever larger numbers of sockets and. We introduce a novel hybrid design for memory accesses that handles the burst mode in traversal based algorithms, like bfs and sssp, and reduces the number of remote accesses and updates. All the designs and optimizations are generic, independent of the graph algorithms, and are part of our numaaware graph processing.
Why existing contentionaware algorithms may hurt performance on numa systems. Our overarching goal is to devise a methodology for developing parallel algorithms addressing these. A case for numaaware contention management on multicore. Our data placement scheme guarantees that all accesses to task output data target the. The second option is to use existing concurrent data structures oblivious of numacalled uniform memory access uma structuresincluding lockbased, lockfree, and waitfree algorithms. Numaaware sharedmemory collective communication for mpi. Pdf multicore nodes with nonuniform memory access numa are. This section describes the algorithms and settings used by esxi to maximize application performance while still maintaining resource guarantees. The kernels support for numa systems has continued to evolve over the lifespan of the 2. In order to maximize processing speed, each partition should take the same. Their paper presents an extension of their openmp runtime system that connects a thread scheduler to a numaaware memory management system, which they choose to call forestgomp. Unlike existing numaaware solutions for data structures.
Numa machines and handle different properties of graphs. They identify threads that are sharing a memory domain and hurting each others. It is a numa aware scheduler that implements workstealing, data distribution and more. Numaaware locks that is as simple as it is powerful. Accelerating bfs and sssp on a numa machine for the graph500. The importance of such numa aware algorithm designs will only.
The toolbox is designed with graphical users interfaces guis and it can be readily used with little knowledge of genetic algorithms and evolutionary programming. This paper makes the case that data management systems need to employ designs that take into consideration the characteristics of modern numa hardware. Numaaware scheduling for both memory and computebound tasks. Numaaware multicore blas matrix multiplication 3 implements mesif modi ed, exclusive, shared, invalid, forward protocol in intel nehalem a processors to maintain the data coherency 2 3. Numaaware datatransfer measurements for powernvlink. In response, we present the morseldriven query execution framework, where scheduling becomes a. However, recent work on arbitration policies in the processorinterconnect 24 shows that when most but not all memory accesses are localwhich is exactly the situation for many numaaware algorithmshardware can unfairly. Often the referenced article could have been placed in more than one category. It is a numaaware scheduler that implements workstealing, data distribution and more. Scalable and low synchronization numaaware algorithm. Numaaware sharedmemory collective communication for. In recent years, a new breed of nonuniform memory access numasystems has emerged. The algorithm gets the type of each thread in the source code based on a static analysis of the.
The concept of a synchronizationfree fastpath previously. Our new cohorting technique allows us to easily create numaaware versions of the tatasbackoff, clh, mcs, and ticket locks, to name a few. Nr is best suited for contended data structures, where it can outperform lockfree algorithms by 3. Empirical memoryaccess cost models in multicore numa architectures. Modifying the openmp program to allocate memory with affinity to each thread adds significant complexity see fig. Similarly to their work, our algorithm s data allocation scheme is designed to reduce interchip communication. Citeseerx document details isaac councill, lee giles, pradeep teregowda. Numaaware scheduling and memory allocation for dataflow. Results are reported in section 8 and section 9 concludes with some remarks. All the algorithms discussed in the following subsections are numa aware to reduce intersocket memory tra c. Design of kernel algorithms and data structures, as well as data placement selection of kernel or application io paths the linux kernels numa features evolved throughout the linux 2.
Lads layoutaware data scheduling is an endtoend data transfer tool optimized for terabit network using a layoutaware data scheduling via pfs. An overview numa becomes more common because memory controllers get close to execution units on microprocessors. Blackbox concurrent data structures for numa architectures. As far as i can tell the drop in performance in such algorithms is caused by nonlocal memory access from a second numa node. One io thread is sufficient for networking traffic pin io thread to device numa node let the scheduler migrate io intensive vm to device numa node high load. For this reason, the placement of threads and memory plays a crucial role in performance.
However, it does not consider the numa nonuniform memory access architecture. Numaaware graph mining techniques for performance and energy. Finally, as most graph algorithms converge asymmet rically, using a single data structure for runtime states may drasti cally degrade the performance, especially. Numaaware graph mining techniques for performance and energy ef. The second option is to use existing concurrent data structures oblivious of numa called uniform memory access uma structuresincluding lockbased, lockfree, and waitfree algorithms. Sep 17, 2015 this document presents a list of articles on numa nonuniform memory architecture that the author considers particularly useful. Our new cohorting technique allows us to easily create numa aware versions of the tatasbackoff, clh, mcs, and ticket locks, to name a few.
Red hat enterprise linux numa support for hpe proliant servers. Briefly, nr implements a numa aware shared log, and then uses the log to replicate data structures consistently across numa nodes. Why existing contention aware algorithms may hurt performance on numa systems. Numaaware datatransfer measurements for powernvlink multi. The importance of such numaaware algorithm designs will only. High performance scalable skip list for numa drops. Our adaptive data placement algorithm tracks the resource utilization of tasks, partitions of tables and table groups, and sockets.
Are readonly accesses to nonlocal memory somehow transparently accelerated e. This white paper discusses linux support for hpe proliant servers. Our runtime algorithms for numaaware task and data placement are fully automatic, applicationindependent, performanceportable across numa machines, and adapt to dynamic changes. Nonuniform memory access numa is a computer memory design used in multiprocessing, where the memory access time depends on the memory location relative to the processor. Numaaware thread scheduling for big data transfers over. In addition, by avoiding unnecessary migrations, our algorithm incurs.
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