Q100

Wu, Lisa; Lottarini, Andrea; Paine, Timothy K.; Kim, Martha A.; Ross, Kenneth A.

doi:10.1145/2541940.2541961

Cited by 118 publications

(5 citation statements)

References 25 publications

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“…Domain-specific processors are proposed in References [1,27,34,57], including Q100 [81] and LINQits [21]. Q100 is a data-flow style processor and uses ASIC functional units to exploit operator pipelines without supporting data management in off-chip storage.…”

Section: Related Workmentioning

confidence: 99%

Accelerating In-Memory Database Selections Using Latency Masking Hardware Threads

Budhkar

Absalyamov

Zois

et al. 2019

ACM Trans. Archit. Code Optim.

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Inexpensive DRAMs have created new opportunities for in-memory data analytics. However, the major bottleneck in such systems is high memory access latency. Traditionally, this problem is solved with large cache hierarchies that only benefit regular applications. Alternatively, many data-intensive applications exhibit irregular behavior. Hardware multithreading can better cope with high latency seen in such applications. This article implements a multithreaded prototype (MTP) on FPGAs for the relational selection operator that exhibits control flow irregularity. On a standard TPC-H query evaluation, MTP achieves a bandwidth utilization of 83%, while the CPU and the GPU implementations achieve 61% and 64%, respectively. Besides being bandwidth efficient, MTP is also 14.2× and 4.2× more power efficient than CPU and GPU, respectively.

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Section: Related Workmentioning

confidence: 99%

Accelerating In-Memory Database Selections Using Latency Masking Hardware Threads

Budhkar

Absalyamov

Zois

et al. 2019

ACM Trans. Archit. Code Optim.

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“…Data analytics acceleration. With data analytics taking center stage in modern computing, there have been proposals for analytics accelerators from both the industry and academia, such as Oracle's DAX [61], Widx [39] and Q100 [69]. Unlike these accelerators, Mondrian's compute units are specifically designed to fit under NMP's tight area and power constraints.…”

Section: Related Workmentioning

confidence: 99%

The Mondrian Data Engine

Drumond¹,

Daglis²,

Mirzadeh³

et al. 2017

Proceedings of the 44th Annual International Symposium on Computer Architecture

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The increasing demand for extracting value out of ever-growing data poses an ongoing challenge to system designers, a task only made trickier by the end of Dennard scaling. As the performance density of traditional CPU-centric architectures stagnates, advancing compute capabilities necessitates novel architectural approaches. Near-memory processing (NMP) architectures are reemerging as promising candidates to improve computing efficiency through tight coupling of logic and memory. NMP architectures are especially fitting for data analytics, as they provide immense bandwidth to memory-resident data and dramatically reduce data movement, the main source of energy consumption.Modern data analytics operators are optimized for CPU execution and hence rely on large caches and employ random memory accesses. In the context of NMP, such random accesses result in wasteful DRAM row buffer activations that account for a significant fraction of the total memory access energy. In addition, utilizing NMP's ample bandwidth with fine-grained random accesses requires complex hardware that cannot be accommodated under NMP's tight area and power constraints. Our thesis is that efficient NMP calls for an algorithm-hardware co-design that favors algorithms with sequential accesses to enable simple hardware that accesses memory in streams. We introduce an instance of such a co-designed NMP architecture for data analytics, the Mondrian Data Engine. Compared to a CPU-centric and a baseline NMP system, the Mondrian Data Engine improves the performance of basic data analytics operators by up to 49× and 5×, and efficiency by up to 28× and 5×, respectively.

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“…The poster [24] hints at the potential of the use of HLS to fully implement queries for inmemory databases by employing Vivado HLS. The integration of custom units in general purpose processors to accelerate analytics workloads and database operations has also been a recent topic of interest (e.g., [25]). Our approach is different from all these works.…”

Section: Accelerators For Databasesmentioning

confidence: 99%

High level synthesis of RDF queries for graph analytics

Castellana

Minutoli

Morari

et al. 2015

2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

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Abstract-In this paper we present a set of techniques that enable the synthesis of efficient custom accelerators for memory intensive, irregular applications. To address the challenges of irregular applications (large memory footprint, unpredictable finegrained data accesses, and high synchronization intensity), and exploit their opportunities (thread level parallelism, memory level parallelism), we propose a novel accelerator design that employs an adaptive and Distributed Controller (DC) architecture, and a Memory Interface Controller (MIC) that supports concurrent and atomic memory operations on a multi-ported/multi-banked shared memory. Among the multitude of algorithms that may benefit from our solution, we focus on the acceleration of graph analytics applications and, in particular, on the synthesis of SPARQL queries on Resource Description Framework (RDF) databases. We achieve this objective by incorporating the synthesis techniques into Bambu, an Open Source high-level synthesis tools, and interfacing it with GEMS, the Graph database Engine for Multithreaded Systems. The GEMS' front-end generates optimized C implementations of the input queries, modeled as graph pattern matching algorithms, which are then automatically synthesized by Bambu. We validate our approach by synthesizing several SPARQL queries from the Lehigh University Benchmark (LUBM).

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Q100

Cited by 118 publications

References 25 publications

Accelerating In-Memory Database Selections Using Latency Masking Hardware Threads

Accelerating In-Memory Database Selections Using Latency Masking Hardware Threads

The Mondrian Data Engine

High level synthesis of RDF queries for graph analytics

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