Service-oriented computing

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.

/pdf/a-scalable-processing-in-memory-accelerator-for-parallel-23yasy5934.pdf

A scalable processing-in-memory accelerator for parallel graph processing

X-Stream is a system for processing both in-memory and out-of-core graphs on a single shared-memory machine. While retaining the scatter-gather programming model with state stored in the vertices, X-Stream is novel in (i) using an edge-centric rather than a vertex-centric implementation of this model, and (ii) streaming completely unordered edge lists rather than performing random access. This design is motivated by the fact that sequential bandwidth for all storage media (main memory, SSD, and magnetic disk) is substantially larger than random access bandwidth.We demonstrate that a large number of graph algorithms can be expressed using the edge-centric scatter-gather model. The resulting implementations scale well in terms of number of cores, in terms of number of I/O devices, and across different storage media. X-Stream competes favorably with existing systems for graph processing. Besides sequential access, we identify as one of the main contributors to better performance the fact that X-Stream does not need to sort edge lists during preprocessing.

/pdf/x-stream-edge-centric-graph-processing-using-streaming-20js8uf00q.pdf

X-Stream: edge-centric graph processing using streaming partitions

As architecture, systems, and data management communities pay greater attention to innovative big data systems and architecture, the pressure of benchmarking and evaluating these systems rises. However, the complexity, diversity, frequently changed workloads, and rapid evolution of big data systems raise great challenges in big data benchmarking. Considering the broad use of big data systems, for the sake of fairness, big data benchmarks must include diversity of data and workloads, which is the prerequisite for evaluating big data systems and architecture. Most of the state-of-the-art big data benchmarking efforts target evaluating specific types of applications or system software stacks, and hence they are not qualified for serving the purposes mentioned above.

http://prof.ict.ac.cn/BigDataBench/old/2.0/wp-content/uploads/2013/10/Wang_BigDataBench.pdf

BigDataBench: A big data benchmark suite from internet services

Ensuring the quality of service (QoS) for latency-sensitive applications while allowing co-locations of multiple applications on servers is critical for improving server utilization and reducing cost in modern warehouse-scale computers (WSCs). Recent work relies on static profiling to precisely predict the QoS degradation that results from performance interference among co-running applications to increase the number of "safe" co-locations. However, these static profiling techniques have several critical limitations: 1) a priori knowledge of all workloads is required for profiling, 2) it is difficult for the prediction to capture or adapt to phase or load changes of applications, and 3) the prediction technique is limited to only two co-running applications.To address all of these limitations, we present Bubble-Flux, an integrated dynamic interference measurement and online QoS management mechanism to provide accurate QoS control and maximize server utilization. Bubble-Flux uses a Dynamic Bubble to probe servers in real time to measure the instantaneous pressure on the shared hardware resources and precisely predict how the QoS of a latency-sensitive job will be affected by potential co-runners. Once "safe" batch jobs are selected and mapped to a server, Bubble-Flux uses an Online Flux Engine to continuously monitor the QoS of the latency-sensitive application and control the execution of batch jobs to adapt to dynamic input, phase, and load changes to deliver satisfactory QoS. Batch applications remain in a state of flux throughout execution. Our results show that the utilization improvement achieved by Bubble-Flux is up to 2.2x better than the prior static approach.

/pdf/bubble-flux-precise-online-qos-management-for-increased-3mvjxzyrb8.pdf

Bubble-flux: precise online QoS management for increased utilization in warehouse scale computers

Emerging scale-out workloads require extensive amounts of computational resources. However, data centers using modern server hardware face physical constraints in space and power, limiting further expansion and calling for improvements in the computational density per server and in the per-operation energy. Continuing to improve the computational resources of the cloud while staying within physical constraints mandates optimizing server efficiency to ensure that server hardware closely matches the needs of scale-out workloads.In this work, we introduce CloudSuite, a benchmark suite of emerging scale-out workloads. We use performance counters on modern servers to study scale-out workloads, finding that today's predominant processor micro-architecture is inefficient for running these workloads. We find that inefficiency comes from the mismatch between the workload needs and modern processors, particularly in the organization of instruction and data memory systems and the processor core micro-architecture. Moreover, while today's predominant micro-architecture is inefficient when executing scale-out workloads, we find that continuing the current trends will further exacerbate the inefficiency in the future. In this work, we identify the key micro-architectural needs of scale-out workloads, calling for a change in the trajectory of server processors that would lead to improved computational density and power efficiency in data centers.

/pdf/clearing-the-clouds-a-study-of-emerging-scale-out-workloads-4tg9zvrf1a.pdf

Clearing the clouds: a study of emerging scale-out workloads on modern hardware

/pdf/2012-clearing-the-clouds-a-study-of-emerging-scale-out-1keqk5h4cz.pdf

(2012) Clearing the Clouds: A Study of Emerging Scale-out Workloads on Modern Hardware

Emerging scale-out cloud applications need extensive amounts of computational resources. However, data centers using modern server hardware face physical constraints in space and power, limiting further expansion and calling for improvements in the computational density per server and in the per-operation energy use. Therefore, continuing to improve the computational resources of the cloud while staying within physical constraints mandates optimizing server efficiency to ensure that server hardware closely matches the needs of scale-out cloud applications. We use performance counters on modern servers to study a wide range of cloud applications, finding that today’s predominant processor architecture is inefficient for running these workloads. We find that inefficiency comes from the mismatch between the application needs and modern processors, particularly in the organization of instruction and data memory systems and the processor core architecture. Moreover, while today’s predominant architectures are inefficient when executing scale-out cloud applications, we find that the current hardware trends further exacerbate the mismatch. In this work, we identify the key micro-architectural needs of cloud applications, calling for a change in the trajectory of server processors that would lead to improved computational density and power efficiency in data centers.

Clearing the Clouds: A Study of Emerging Workloads on Modern Hardware

Emerging scale-out workloads require extensive amounts of computational resources. However, data centers using modern server hardware face physical constraints in space and power, limiting further expansion and calling for improvements in the computational density per server and in the per-operation energy. Continuing to improve the computational resources of the cloud while staying within physical constraints mandates optimizing server efficiency to ensure that server hardware closely matches the needs of scale-out workloads.In this work, we introduce CloudSuite, a benchmark suite of emerging scale-out workloads. We use performance counters on modern servers to study scale-out workloads, finding that today’s predominant processor microarchitecture is inefficient for running these workloads. We find that inefficiency comes from the mismatch between the workload needs and modern processors, particularly in the organization of instruction and data memory systems and the processor core microarchitecture. Moreover, while today’s predominant microarchitecture is inefficient when executing scale-out workloads, we find that continuing the current trends will further exacerbate the inefficiency in the future. In this work, we identify the key microarchitectural needs of scale-out workloads, calling for a change in the trajectory of server processors that would lead to improved computational density and power efficiency in data centers.

Quantifying the Mismatch between Emerging Scale-Out Applications and Modern Processors

Emerging scale-out workloads need extensive amounts of computational resources. However, datacenters using modern server hardware face physical constraints in space and power, limiting further expansion and requiring improvements in the computational density per server and in the per-operation energy. Continuing to improve the computational resources of the cloud while staying within physical constraints mandates optimizing server efficiency. In this work, we demonstrate that modern server processors are highly inefficient for running cloud workloads. To address this problem, we investigate the microarchitectural behavior of scale-out workloads and present opportunities to enable specialized processor designs that closely match the needs of the cloud.

/pdf/a-case-for-specialized-processors-for-scale-out-workloads-4tqqctbp3j.pdf

Mohammad Alisafaee

Papers

Clearing the clouds: a study of emerging scale-out workloads on modern hardware

(2012) Clearing the Clouds: A Study of Emerging Scale-out Workloads on Modern Hardware

Clearing the Clouds: A Study of Emerging Workloads on Modern Hardware

Quantifying the Mismatch between Emerging Scale-Out Applications and Modern Processors

A Case for Specialized Processors for Scale-Out Workloads