RELEASE: A High-level Paradigm for Reliable Large-scale Server Software

Erlang is a functional language with a much-emulated model for building reliable distributed systems. This paper outlines the RELEASE project, and describes the progress in the rst six months. The project aim is to scale the Erlang's radical concurrency-oriented programming paradigm to build reliable general-purpose software, such as server-based systems, on massively parallel machines. Currently Erlang has inherently scalable computation and reliability models, but in practice scalability is constrained by aspects of the language and virtual machine. We are working at three levels to address these challenges: evolving the Erlang virtual machine so that it can work effectively on large scale multicore systems; evolving the language to Scalable Distributed (SD) Erlang; developing a scalable Erlang infrastructure to integrate multiple, heterogeneous clusters. We are also developing state of the art tools that allow programmers to understand the behaviour of massively parallel SD Erlang programs. We will demonstrate the e ectiveness of the RELEASE approach using demonstrators and two large case studies on a Blue Gene

RELEASE: A High-level Paradigm for Reliable Large-scale Server Software

Erlang is a functional language with a much-emulated model for building reliable distributed systems. This paper outlines the RELEASE project, and describes the progress in the first six months. The project aim is to scale the Erlang’s radical concurrency-oriented programming paradigm to build reliable general-purpose software, such as server-based systems, on massively parallel machines. Currently Erlang has inherently scalable computation and reliability models, but in practice scalability is constrained by aspects of the language and virtual machine. We are working at three levels to address these challenges: evolving the Erlang virtual machine so that it can work effectively on large scale multicore systems; evolving the language to Scalable Distributed (SD) Erlang; developing a scalable Erlang infrastructure to integrate multiple, heterogeneous clusters. We are also developing state of the art tools that allow programmers to understand the behaviour of massively parallel SD Erlang programs. We will demonstrate the effectiveness of the RELEASE approach using demonstrators and two large case studies on a Blue Gene

Contention-aware performance monitoring counter support for real-time MPSoCs

Tasks running in MPSoCs experience contention delays when accessing MPSoC’s shared resources, complicating task timing analysis and deriving execution time bounds. Understanding the Actual Contention Delay (ACD) each task suffers due to other corunning tasks, and the particular hardware shared resources in which contention occurs, is of prominent importance to increase confidence on derived execution time bounds of tasks. And, whenever those bounds are violated, ACD provides information on the reasons for overruns. Unfortunately, existing MPSoC designs considered in real-time domains offer limited hardware support to measure tasks’ ACD losing all these potential benefits. In this paper we propose the Contention Cycle Stack (CCS), a mechanism that extends performance monitoring counters to track specific events that allow estimating the ACD that each task suffers from every contending task on every hardware shared resource. We build the CCS using a set of specialized low-overhead Performance Monitoring Counters for the Cobham Gaisler GR740 (NGMP) MPSoC – used in the space domain – for which we show CCS’s benefits.The research leading to these results has received funding from the European Space Agency under contracts 4000109680, 4000110157 and NPI 4000102880, and the Ministry of Science and Technology of Spain under contract TIN-2015-65316-P. Jaume Abella has been partially supported by the Ministry of Economy and Competitiveness under Ramon y Cajal postdoctoral fellowship number RYC-2013-14717.Peer ReviewedPostprint (author's final draft

Dynamic Information Flow Tracking on Multicores

Dynamic Information Flow Tracking (DIFT) is a promising technique for detecting software attacks. Due to the computationally intensive nature of the technique, prior efficient implementations [21, 6] rely on specialized hardware support whose only purpose is to enable DIFT. Alternatively, prior software implementations are either too slow [17, 15] resulting in execution time increases as much as four fold for SPEC integer programs or they are not transparent [31] requiring source code modifications. In this paper, we propose the use of chip multiprocessors (CMP) to perform DIFT transparently and efficiently. We spawn a helper thread that is scheduled on a separate core and is only responsible for performing information flow tracking operations. This entails the communication of registers and flags between the main and helper threads. We explore software (shared memory) and hardware (dedicated interconnect) approaches to enable this communication. Finally, we propose a novel application of the DIFT infrastructure where, in addition to the detection of the software attack, DIFT assists in the process of identifying the cause of the bug in the code that enabled the exploit in the first place. We conducted detailed simulations to evaluate the overhead for performing DIFT and found that to be 48 % for SPEC integer programs

MARACAS: a real-time multicore VCPU scheduling framework

This paper describes a multicore scheduling and load-balancing framework called MARACAS, to address shared cache and memory bus contention. It builds upon prior work centered around the concept of virtual CPU (VCPU) scheduling. Threads are associated with VCPUs that have periodically replenished time budgets. VCPUs are guaranteed to receive their periodic budgets even if they are migrated between cores. A load balancing algorithm ensures VCPUs are mapped to cores to fairly distribute surplus CPU cycles, after ensuring VCPU timing guarantees. MARACAS uses surplus cycles to throttle the execution of threads running on specific cores when memory contention exceeds a certain threshold. This enables threads on other cores to make better progress without interference from co-runners. Our scheduling framework features a novel memory-aware scheduling approach that uses performance counters to derive an average memory request latency. We show that latency-based memory throttling is more effective than rate-based memory access control in reducing bus contention. MARACAS also supports cache-aware scheduling and migration using page recoloring to improve performance isolation amongst VCPUs. Experiments show how MARACAS reduces multicore resource contention, leading to improved task progress.http://www.cs.bu.edu/fac/richwest/papers/rtss_2016.pdfAccepted manuscrip

A design methodology for soft-core platforms on FPGA with SMP Linux, OpenMP support, and distributed hardware profiling system

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PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications

Energy efficiency is a major concern in modern high-performance computing system design. In the past few years, there has been mounting evidence that power usage limits system scale and computing density, and thus, ultimately system performance. However, despite the impact of power and energy on the computer systems community, few studies provide insight to where and how power is consumed on high-performance systems and applications. In previous work, we designed a framework called PowerPack that was the first tool to isolate the power consumption of devices including disks, memory, NICs, and processors in a high-performance cluster and correlate these measurements to application functions. In this work, we extend our framework to support systems with multicore, multiprocessor-based nodes, and then provide in-depth analyses of the energy consumption of parallel applications on clusters of these systems. These analyses include the impacts of chip multiprocessing on power and energy efficiency, and its interaction with application executions. In addition, we use PowerPack to study the power dynamics and energy efficiencies of dynamic voltage and frequency scaling (DVFS) techniques on clusters. Our experiments reveal conclusively how intelligent DVFS scheduling can enhance system energy efficiency while maintaining performance

Securing Network Processors with Hardware Monitors

As an essential part of modern society, the Internet has fundamentally changed our lives during the last decade. Novel applications and technologies, such as online shopping, social networking, cloud computing, mobile networking, etc, have sprung up at an astonishing pace. These technologies not only influence modern life styles but also impact Internet infrastructure. Numerous new network applications and services require better programmability and flexibility for network devices, such as routers and switches. Since traditional fixed function network routers based on application specific integrated circuits (ASICs) have difficulty keeping pace with the growing demands of next-generation Internet applications, there is an ongoing shift in the industry toward implementing network devices using programmable network processors (NPs). While network processors offer great benefits in terms of flexibility, their reprogrammable nature exposes potential security risks. Similar to network end-systems, such as general-purpose computers, software-based network processors have security vulnerabilities that can be attacked remotely. Recent research has shown that a new type of data plane attack is able to modify the functionality of a network processor and cause a denial-of-service (DoS) attack by sending a single malformed UDP packet. Since this attack relies solely on data plane access and does not need access to the control plane, it can be particularly difficult to control. Hardware security monitors have been introduced to identify and eliminate these malicious packets before they can propagate and cause devastating effects in the network. However, previous work on hardware monitors only focus on single core systems with static (or very slowly changing) workloads. In network processors that use up to hundreds of parallel processor cores and have processing workloads that can change dynamically based on the network traffic, the realization of a complete multicore hardware monitoring system remains a critical challenge. Our research work in this thesis provides a comprehensive solution to this problem. Our first contribution is the design and prototype implementation of a Scalable Hardware Monitoring Grid (SHMG). This scalable architecture balances area cost and performance overhead by using a clustered approach for multicore NP systems. In order to adapt to dynamically changing network traffic, a resource reallocation algorithm is designed to reassign the processing resources in SHMG to different network applications at runtime. An evaluation of the prototype SHMG on an Altera DE4 board demonstrates low resource and performance overheads. The functionality and performance of a runtime resource reallocation algorithm are tested using a simulation environment. A second significant contribution of this work is a network system-level security solution for multicore network processors with hardware monitors. It addresses two key problems: (1) how to securely manage and reprogram processor cores and monitors in a deployed router in the network, and (2) how to prevent the large number of identical router devices in the network from an attack that can circumvent one specific monitoring system and lead to Internet-scale failures. A Secure Dynamic Multicore Hardware Monitoring System (SDMMon) is designed based on cryptographic principles and suitable key management to ensure the secure installation of processor binaries and monitor graphs. We present a Merkle tree based parameterizable high performance hash function that can be configured to perform a variety of functions in different devices via a 32-bit configuration parameter. A prototype system composed of both the SDMMon and the parameterizable hash is implemented and evaluated on an Altera DE4 board. Finally, a fully-functional, comprehensive Multicore NP Security Platform, which integrates both the SHMG and the SDMMon security features, has been implemented on an Altera DE5 board

Secure Virtualization and Multicore Platforms State-of-the-Art report

SVaM