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.
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27 Mar 2017
TL;DR: The RowHammer problem as discussed by the authors is the phenomenon that repeatedly accessing a row in a modern DRAM chip causes bit flips in physically-adjacent rows at consistently predictable bit locations.
Abstract: As memory scales down to smaller technology nodes, new failure mechanisms emerge that threaten its correct operation. If such failure mechanisms are not anticipated and corrected, they can not only degrade system reliability and availability but also, perhaps even more importantly, open up security vulnerabilities: a malicious attacker can exploit the exposed failure mechanism to take over the entire system. As such, new failure mechanisms in memory can become practical and significant threats to system security. In this work, we discuss the RowHammer problem in DRAM, which is a prime (and perhaps the first) example of how a circuit-level failure mechanism in DRAM can cause a practical and widespread system security vulnerability. RowHammer, as it is popularly referred to, is the phenomenon that repeatedly accessing a row in a modern DRAM chip causes bit flips in physically-adjacent rows at consistently predictable bit locations. It is caused by a hardware failure mechanism called DRAM disturbance errors, which is a manifestation of circuit-level cell-to-cell interference in a scaled memory technology. Researchers from Google Project Zero recently demonstrated that this hardware failure mechanism can be effectively exploited by user-level programs to gain kernel privileges on real systems. Several other recent works demonstrated other practical attacks exploiting RowHammer. These include remote takeover of a server vulnerable to RowHammer, takeover of a victim virtual machine by another virtual machine running on the same system, and takeover of a mobile device by a malicious user-level application that requires no permissions. We analyze the root causes of the RowHammer problem and examine various solutions. We also discuss what other vulnerabilities may be lurking in DRAM and other types of memories, e.g., NAND flash memory or Phase Change Memory, that can potentially threaten the foundations of secure systems, as the memory technologies scale to higher densities. We conclude by describing and advocating a principled approach to memory reliability and security research that can enable us to better anticipate and prevent such vulnerabilities.
87 citations
01 Jan 2015
TL;DR: A new PIM architecture is proposed that does not change the existing sequential programming models and automatically decides whether to execute PIM operations in memory or processors depending on the locality of data, and combines the best parts of conventional and PlM architectures by adapting to data locality of applications.
Abstract: Processing -in-memory (PIM) is rapidly rising as a viable solution for the memory wall crisis, rebounding from its unsuc cessful attempts in I990s due to practicality concerns, which are alleviated with recent advances in 3D stacking technolo gies. However, it is still challenging to integrate the PIM architectures with existing systems in a seamless manner due to two common characteristics: unconventional progr amming models for in-memor y computation units and lack of ability to utilize large on- chip caches. In this paper, we propose a new PIM architecture that (I) does not change the existing sequential progr amming mod el s and (2) aut omatically decides whet her to exe cut e PIM operations in memory or processor s de pending on the local ity of data. The key idea is to implement simple in-memory computation using compute- capable memory commands and use specialized instructions, which we call PIM- enabled in structions, to invo ke in-memory computation. This allows PIM operations to be interoperable with existing program ming models, cache coherence protocols, and virtual memo ry mechanisms with no modification. In addition, we introduce a simple har dware struct ure that monitors the locality of data acce ssed by a PIM-en abled instruction at runtime to adap tively execut e the instruction at the host processor (instead of in memory) when the instruction can benefit from large
84 citations
Cites background or methods from "A scalable processing-in-memory acc..."
...In addition, we introduce a simple hardware structure that monitors the locality of data accessed by a PIM-enabled instruction at runtime to adaptively execute the instruction at the host processor (instead of in memory) when the instruction can benefit from large on-chip caches....
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...Moreover, most prior approaches perform in-memory computation on noncacheable, physically addressed memory regions, which inevitably sacrifices efficiency and safety of all memory accesses from host processors to memory regions that can potentially be accessed by PIM....
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TL;DR: A realization of stateful reconfigurable logic functions via a single three-terminal magnetic tunnel junction (MTJ) device within a spintronic memory by exploiting the novel voltage-gated spin Hall-effect driven magnetization switching mechanism is reported.
Abstract: Stateful in-memory logic (IML) is a promising paradigm to realize the unity of data storage and processing in the same die, exhibiting great feasibility to break the bottleneck of the conventional von Neumann architecture. On the roadmap toward developing such a logic platform, a critical step is the effective and efficient realization of a complete set of logic functions within a memory. In this paper, we report a realization of stateful reconfigurable logic functions via a single three-terminal magnetic tunnel junction (MTJ) device within a spintronic memory by exploiting the novel voltage-gated spin Hall-effect driven magnetization switching mechanism. This proposed reconfigurable IML methodology can be implemented within either a typical memory array or a cross-point array architecture. The feasibility of the proposed approach is successfully demonstrated with hybrid MTJ/CMOS circuit simulations. We believe our work may promote the research and development of the revolutionary IML for future non-von Neumann architectures.
80 citations
Posted Content•
TL;DR: Ambit, a recently-proposed mechanism to perform bulk bitwise operations completely inside main memory, exploits the internal organization and analog operation of DRAM-based memory to achieve low cost, high performance, and low energy.
Abstract: Many applications heavily use bitwise operations on large bitvectors as part of their computation. In existing systems, performing such bulk bitwise operations requires the processor to transfer a large amount of data on the memory channel, thereby consuming high latency, memory bandwidth, and energy. In this paper, we describe Ambit, a recently-proposed mechanism to perform bulk bitwise operations completely inside main memory. Ambit exploits the internal organization and analog operation of DRAM-based memory to achieve low cost, high performance, and low energy. Ambit exposes a new bulk bitwise execution model to the host processor. Evaluations show that Ambit significantly improves the performance of several applications that use bulk bitwise operations, including databases.
77 citations
Cites background or methods from "A scalable processing-in-memory acc..."
..., [15, 14, 154, 41, 49, 59, 58, 25, 77, 24, 102, 119]) propose processing in the logic layer of 3D-stacked DRAM, which stacks DRAM layers on top of a logic layer (e....
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...In contrast to Processing in Memory architectures [15, 14, 154, 49, 157, 141, 58, 43, 108, 57, 42, 41, 20, 16, 88, 139, 137, 118, 85, 115, 46, 39, 132, 133, 59, 53, 52, 24, 102, 25, 112, 26] that add extra computational logic closer to main memory, the idea behind Processing using Memory is to exploit the existing structure and organization of memory devices with minimal changes to provide additional functionality....
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TL;DR: RowClone, a mechanism that exploits DRAM technology to perform bulk copy and initialization operations completely inside main memory, and a complementary work that uses DRAM to performs bulk bitwise AND and OR operations inside mainmemory significantly improve the performance and energy efficiency of the respective operations.
Abstract: In existing systems, the off-chip memory interface allows the memory controller to perform only read or write operations. Therefore, to perform any operation, the processor must first read the source data and then write the result back to memory after performing the operation. This approach consumes high latency, bandwidth, and energy for operations that work on a large amount of data. Several works have proposed techniques to process data near memory by adding a small amount of compute logic closer to the main memory chips. In this chapter, we describe two techniques proposed by recent works that take this approach of processing in memory further by exploiting the underlying operation of the main memory technology to perform more complex tasks. First, we describe RowClone, a mechanism that exploits DRAM technology to perform bulk copy and initialization operations completely inside main memory. We then describe a complementary work that uses DRAM to perform bulk bitwise AND and OR operations inside main memory. These two techniques significantly improve the performance and energy efficiency of the respective operations.
75 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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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.
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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....
[...]
...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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