A scalable processing-in-memory accelerator for parallel graph processing
13 Jun 2015-Vol. 43, Iss: 3, pp 105-117
TL;DR: This work argues that the conventional concept of processing-in-memory (PIM) can be a viable solution to achieve memory-capacity-proportional performance and designs a programmable PIM accelerator for large-scale graph processing called Tesseract.
Abstract: The explosion of digital data and the ever-growing need for fast data analysis have made in-memory big-data processing in computer systems increasingly important. In particular, large-scale graph processing is gaining attention due to its broad applicability from social science to machine learning. However, scalable hardware design that can efficiently process large graphs in main memory is still an open problem. Ideally, cost-effective and scalable graph processing systems can be realized by building a system whose performance increases proportionally with the sizes of graphs that can be stored in the system, which is extremely challenging in conventional systems due to severe memory bandwidth limitations. In this work, we argue that the conventional concept of processing-in-memory (PIM) can be a viable solution to achieve such an objective. The key modern enabler for PIM is the recent advancement of the 3D integration technology that facilitates stacking logic and memory dies in a single package, which was not available when the PIM concept was originally examined. In order to take advantage of such a new technology to enable memory-capacity-proportional performance, we design a programmable PIM accelerator for large-scale graph processing called Tesseract. Tesseract is composed of (1) a new hardware architecture that fully utilizes the available memory bandwidth, (2) an efficient method of communication between different memory partitions, and (3) a programming interface that reflects and exploits the unique hardware design. It also includes two hardware prefetchers specialized for memory access patterns of graph processing, which operate based on the hints provided by our programming model. Our comprehensive evaluations using five state-of-the-art graph processing workloads with large real-world graphs show that the proposed architecture improves average system performance by a factor of ten and achieves 87% average energy reduction over conventional systems.
Citations
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29 Oct 2022
TL;DR: Gzippo as mentioned in this paper employs a tandem-isomorphic crossbar architecture both to eliminate redundant searches and sequential indexing during iterations, and to remove sparsity leading to non-effective computation on zero values.
Abstract: Graph application plays a significant role in real-world data computation. However, the memory access patterns become the performance bottleneck of the graph applications, which include low compute-to-communication ratio, poor temporal locality, and poor spatial locality. Existing RRAM-based processing-in-memory accelerators reduce the data movements but fail to address both sparsity and redundancy of graph data. In this work, we present Gzippo, a highly-compact design that supports graph computation in the compressed sparse format. Gzippo employs a tandem-isomorphic-crossbar architecture both to eliminate redundant searches and sequential indexing during iterations, and to remove sparsity leading to non-effective computation on zero values. Gzippo achieves a 3.0× (up to 17.4×) performance speedup, 23.9× (up to 163.2×) energy efficiency over state-of-the-art RRAM-based PIM accelerator, respectively.
1 citations
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11 Jul 2022
TL;DR: In this paper , the authors propose and empirically evaluate hybrid data structures, which are concurrent data structures custom-designed for near-memory processing (NMP) architectures, such as skiplists and B+ trees.
Abstract: In recent years, the ever-increasing impact of memory access bottlenecks has brought forth a renewed interest in near-memory processing (NMP) architectures. In this work, we propose and empirically evaluate hybrid data structures, which are concurrent data structures custom-designed for these new NMP architectures. We focus on cache-optimized data structures, such as skiplists and B+ trees, that are often used as index structures in online transaction processing (OLTP) systems to enable fast key-based lookups. These data structures are hierarchical, where lookups begin at a small number of top-level nodes and diverge to many different node paths as they move down the hierarchy, such that nodes in higher levels benefit more from caching. Our proposed hybrid data structures split traditional hierarchical data structures into a host-managed portion consisting of higher-level nodes and an NMP-managed portion consisting of the remaining lower-level nodes, thus retaining and further enhancing the cache-conscious optimizations of their conventional implementations. Although the idea might seem relatively simple, the splitting of the data structure prompts new synchronization problems, and careful implementation is required to ensure high concurrency and correctness. We provide implementations of a hybrid skiplist and a hybrid B+ tree, and we empirically evaluate them on a cycle-accurate full-system architecture simulator. Our results show that the hybrid data structures have the potential to improve performance by more than 2x compared to state-of-the-art concurrent data structures.
1 citations
Proceedings Article•
01 Oct 2016TL;DR: This talk first discusses major challenges modern memory systems face in the presence of increasing demand for data and its fast analysis, and examines some promising research and design directions to overcome these challenges and enable scalable memory systems for the future.
Abstract: The memory system is a fundamental performance and energy bottleneck in almost all computing systems. Recent system design, application, and technology trends that require more capacity, bandwidth, efficiency, and predictability out of the memory system make it an even more important system bottleneck [36, 38]. At the same time, DRAM and flash technologies are experiencing difficult technology scaling challenges that make the maintenance and enhancement of their capacity, energy efficiency, and reliability significantly more costly with conventional techniques (see, for example [13, 17–19, 23, 25, 26, 29, 30, 40]). In fact, recent reliability issues with DRAM, such as the RowHammer problem [23], are already threatening system security, predictability and robustness. In this talk, we first discuss major challenges modern memory systems face in the presence of increasing demand for data and its fast analysis. We then examine some promising research and design directions to overcome these challenges and enable scalable memory systems for the future. We discuss three key solution directions: 1) enabling new memory architectures, functions, interfaces, and better integration of memory and the rest of the system (e.g., [1, 2, 4, 12, 22, 27, 28, 32, 42–44]), 2) designing a memory system that intelligently employs emerging non-volatile memory (NVM) technologies (e.g., [24–26, 31, 33, 41, 49–51]), 3) reducing memory interference and providing predictable performance to applications sharing the memory system (e.g., [3, 14–16, 20–22, 35, 37, 39, 45– 48]). If time permits, we will also touch upon our ongoing related work in combating scaling challenges of NAND flash memory (e.g., [5–11, 34]).
1 citations
01 Jun 2022
TL;DR: The main memory bottleneck in computing systems is an increasingly worsening bottleneck and DRAM vendors often prioritize memory capacity scaling over latency and bandwidth over memory bandwidth and bandwidth.
Abstract: DRAM-based main memory is used in nearly all computing systems as a major component. Modern memory-intensive workloads have increasing memory bandwidth, latency, and capacity requirements. However, DRAM vendors often prioritize memory capacity scaling over latency and bandwidth [1]–[4]. As a result, main memory is an increasingly worsening bottleneck in computing systems [3,5–9].
1 citations
TL;DR: Sim2PIM as mentioned in this paper is a simple simulator for PIM devices that seamlessly integrates any PIM architecture with the host processor and memory hierarchy, achieving execution speeds and accuracy on par with the perf tool on host code.
1 citations
References
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TL;DR: This paper provides an in-depth description of Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and looks at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
Abstract: In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/. To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
14,696 citations
"A scalable processing-in-memory acc..." refers methods in this paper
...Our comprehensive evaluations using five state-of-the-art graph processing workloads with large real-world graphs show that the proposed architecture improves average system performance by a factor of ten and achieves 87% average energy reduction over conventional systems....
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Journal Article•
TL;DR: Google as discussed by the authors is a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems.
13,327 citations
TL;DR: This work presents a new coarsening heuristic (called heavy-edge heuristic) for which the size of the partition of the coarse graph is within a small factor of theSize of the final partition obtained after multilevel refinement, and presents a much faster variation of the Kernighan--Lin (KL) algorithm for refining during uncoarsening.
Abstract: Recently, a number of researchers have investigated a class of graph partitioning algorithms that reduce the size of the graph by collapsing vertices and edges, partition the smaller graph, and then uncoarsen it to construct a partition for the original graph [Bui and Jones, Proc. of the 6th SIAM Conference on Parallel Processing for Scientific Computing, 1993, 445--452; Hendrickson and Leland, A Multilevel Algorithm for Partitioning Graphs, Tech. report SAND 93-1301, Sandia National Laboratories, Albuquerque, NM, 1993]. From the early work it was clear that multilevel techniques held great promise; however, it was not known if they can be made to consistently produce high quality partitions for graphs arising in a wide range of application domains. We investigate the effectiveness of many different choices for all three phases: coarsening, partition of the coarsest graph, and refinement. In particular, we present a new coarsening heuristic (called heavy-edge heuristic) for which the size of the partition of the coarse graph is within a small factor of the size of the final partition obtained after multilevel refinement. We also present a much faster variation of the Kernighan--Lin (KL) algorithm for refining during uncoarsening. We test our scheme on a large number of graphs arising in various domains including finite element methods, linear programming, VLSI, and transportation. Our experiments show that our scheme produces partitions that are consistently better than those produced by spectral partitioning schemes in substantially smaller time. Also, when our scheme is used to compute fill-reducing orderings for sparse matrices, it produces orderings that have substantially smaller fill than the widely used multiple minimum degree algorithm.
5,629 citations
"A scalable processing-in-memory acc..." refers methods in this paper
...For this purpose, we use METIS [27] to perform 512-way multi-constraint partitioning to balance the number of vertices, outgoing edges, and incoming edges of each partition, as done in a recent previous work [51]....
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...This is confirmed by the observation that Tesseract with METIS spends 59% of execution time waiting for synchronization barriers....
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12 Jun 2005
TL;DR: The goals are to provide easy-to-use, portable, transparent, and efficient instrumentation, and to illustrate Pin's versatility, two Pintools in daily use to analyze production software are described.
Abstract: Robust and powerful software instrumentation tools are essential for program analysis tasks such as profiling, performance evaluation, and bug detection. To meet this need, we have developed a new instrumentation system called Pin. Our goals are to provide easy-to-use, portable, transparent, and efficient instrumentation. Instrumentation tools (called Pintools) are written in C/C++ using Pin's rich API. Pin follows the model of ATOM, allowing the tool writer to analyze an application at the instruction level without the need for detailed knowledge of the underlying instruction set. The API is designed to be architecture independent whenever possible, making Pintools source compatible across different architectures. However, a Pintool can access architecture-specific details when necessary. Instrumentation with Pin is mostly transparent as the application and Pintool observe the application's original, uninstrumented behavior. Pin uses dynamic compilation to instrument executables while they are running. For efficiency, Pin uses several techniques, including inlining, register re-allocation, liveness analysis, and instruction scheduling to optimize instrumentation. This fully automated approach delivers significantly better instrumentation performance than similar tools. For example, Pin is 3.3x faster than Valgrind and 2x faster than DynamoRIO for basic-block counting. To illustrate Pin's versatility, we describe two Pintools in daily use to analyze production software. Pin is publicly available for Linux platforms on four architectures: IA32 (32-bit x86), EM64T (64-bit x86), Itanium®, and ARM. In the ten months since Pin 2 was released in July 2004, there have been over 3000 downloads from its website.
4,019 citations
"A scalable processing-in-memory acc..." refers methods in this paper
...We evaluate our architecture using an in-house cycle-accurate x86-64 simulator whose frontend is Pin [38]....
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06 Jun 2010
TL;DR: A model for processing large graphs that has been designed for efficient, scalable and fault-tolerant implementation on clusters of thousands of commodity computers, and its implied synchronicity makes reasoning about programs easier.
Abstract: Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs - in some cases billions of vertices, trillions of edges - poses challenges to their efficient processing. In this paper we present a computational model suitable for this task. Programs are expressed as a sequence of iterations, in each of which a vertex can receive messages sent in the previous iteration, send messages to other vertices, and modify its own state and that of its outgoing edges or mutate graph topology. This vertex-centric approach is flexible enough to express a broad set of algorithms. The model has been designed for efficient, scalable and fault-tolerant implementation on clusters of thousands of commodity computers, and its implied synchronicity makes reasoning about programs easier. Distribution-related details are hidden behind an abstract API. The result is a framework for processing large graphs that is expressive and easy to program.
3,840 citations
"A scalable processing-in-memory acc..." refers methods in this paper
...Our comprehensive evaluations using five state-of-the-art graph processing workloads with large real-world graphs show that the proposed architecture improves average system performance by a factor of ten and achieves 87% average energy reduction over conventional systems....
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...It also includes two hardware prefetchers specialized for memory access patterns of graph processing, which operate based on the hints provided by our programming model....
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