SimBench: A Portable Benchmarking Methodology for Full-System Simulators

We acknowledge funding by the EPSRC grant PAMELA EP/K008730/1.Full-system simulators are increasingly finding their way into the consumer space for the purposes of backwards compatibility and hardware emulation (e.g. for games consoles). For such compute-intensive applications simulation performance is paramount. In this paper we argue that existing benchmarksuites such as SPEC CPU2006, originally designed for architecture and compiler performance evaluation, are not well suited for the identification of performance bottlenecks in full-system simulators. While their large, complex workloads provide an indication as to the performance of the simulator on ‘real-world’ workloads, this does not give any indication of why a particular simulator might run an application faster or slower than another. In this paper we present SimBench, an extensive suite of targeted micro-benchmarks designed to run bare-metal on a fullsystem simulator. SimBench exercises dynamic binary translation (DBT) performance, interrupt and exception handling, memoryaccess performance, I/O and other performance-sensitive areas. SimBench is cross-platform benchmarking framework and can be retargeted to new architectures with minimal effort. For several simulators, including QEMU, Gem5 and SimIt-ARM, and targeting ARM and Intel x86 architectures, we demonstrate that SimBench is capable of accurately pinpointing and explaining real-world performance anomalies, which are largely obfuscated by existing application-oriented benchmarks.Postprin

Efficient bypass mechanisms for low latency networks on-chip

RESUMEN: La importancia de las redes en-chip en los procesadores multi-núcleo es cada vez mayor. Los routers con baipás son una solución eficiente para reducir la latencia de estas redes. Existen dos tipos de redes con baipás: single-hop y multi-hop. Las redes con baipás single-hop minimizan la latencia individual de cada router al asignar los recursos del router con antelación a la recepción de los paquetes. Las redes con baipás multi-hop, conocidas como SMART, permiten que los paquetes atraviesen múltiples routers en un único ciclo. La primera propuesta de esta tesis es Non-Empty Buffer Bypass (NEBB), un mecanismo que incrementa la utilización del baipás de tipo single-hop, eliminando la necesidad de usar canales virtuales. Para redes con baipás multi-hop propone SMART++ y S-SMART++. SMART++ elimina la necesidad de SMART de usar una gran cantidad de canales virtuales para aprovechar el ancho de banda de la red, permitiendo el diseño de configuraciones de bajo coste. S-SMART++ hace uso de la asignación de recursos de forma especulativa para preparar el baipás de tipo multi-hop. Este mecanismo reduce la latencia y su dependencia con la longitud máxima de los saltos de tipo multi-hop, aspecto clave para su viabilidad en diseños reales. La contribución final es un conjunto de herramientas de código abierto llamada Bypass Simulation Toolset (BST) compuesto por versiones extendidas de BookSim y OpenSMART, una API para integrar BookSim en otros simuladores y una serie de scripts para facilitar el diseño y evaluación de este tipo de redes.ABSTRACT: Networks on-Chip (NoCs) are becoming more important in many-core processors as the number of cores grows. Bypass routers are an efficient solution that skips pipeline stages. There are two types of bypass mechanisms: single-hop and multi-hop bypass. Single-hop bypass minimizes the router delay by skipping allocation stages in each hop. Multi-hop bypass, called SMART, minimizes the effective number of hops by traversing multiple routers in a single cycle. The first proposal of this dissertation is Non-Empty Buffer Bypass (NEBB) for single-hop bypass, which increases the bypass utilization without requiring VCs to match traditional bypass routers. It proposes SMART++ and S-SMART++ for multi-hop bypass. SMART++ removes the requirement of using multiple VCs of SMART to exploit the bandwidth of the network, enabling low-cost configurations. S-SMART++ relies on speculative allocation to set up multi-hop bypass paths. Thus, it reduces latency and its dependency with the maximum length of multi-hops, relaxing the requirements to integrate multi-hop bypass in real designs. The final contribution is an open-source set of tools to simulate bypass NoCs called Bypass Simulation Toolset (BST) conformed by extended versions of BookSim and OpenSMART, an API to integrate BookSim in other simulators, and scripts to simplify the designing and evaluation of such NoCs.This work was supported by the Spanish Ministry of Science, Innovation and Universities, FPI grant BES-2017-079971, and contracts TIN2010-21291-C02-02, TIN2013- 46957-C2-2-P, TIN2015-65316-P, TIN2016-76635-C2-2-R (AEI/FEDER, UE) and TIC PID2019-105660RB-C22; the European HiPEAC Network of Excellence; the European Community's Seventh Framework Programme (FP7/2007-2013), under the Mont-Blanc 1 and 2 projects (grant agreements n 288777 and 610402); the European Union's Horizon 2020 research and innovation programme under the Mont-Blanc 3 project (grant agreement nº 671697). Bluespec Inc. provided access to Bluespec tools

AdaptGear: Accelerating GNN Training via Adaptive Subgraph-Level Kernels on GPUs

Graph neural networks (GNNs) are powerful tools for exploring and learning from graph structures and features. As such, achieving high-performance execution for GNNs becomes crucially important. Prior works have proposed to explore the sparsity (i.e., low density) in the input graph to accelerate GNNs, which uses the full-graph-level or block-level sparsity format. We show that they fail to balance the sparsity benefit and kernel execution efficiency. In this paper, we propose a novel system, referred to as AdaptGear, that addresses the challenge of optimizing GNNs performance by leveraging kernels tailored to the density characteristics at the subgraph level. Meanwhile, we also propose a method that dynamically chooses the optimal set of kernels for a given input graph. Our evaluation shows that AdaptGear can achieve a significant performance improvement, up to $6.49 \times$ ($1.87 \times$ on average), over the state-of-the-art works on two mainstream NVIDIA GPUs across various datasets

Accurate Energy and Performance Prediction for Frequency-Scaled GPU Kernels

Energy optimization is an increasingly important aspect of today’s high-performance computing applications. In particular, dynamic voltage and frequency scaling (DVFS) has become a widely adopted solution to balance performance and energy consumption, and hardware vendors provide management libraries that allow the programmer to change both memory and core frequencies manually to minimize energy consumption while maximizing performance. This article focuses on modeling the energy consumption and speedup of GPU applications while using different frequency configurations. The task is not straightforward, because of the large set of possible and uniformly distributed configurations and because of the multi-objective nature of the problem, which minimizes energy consumption and maximizes performance. This article proposes a machine learning-based method to predict the best core and memory frequency configurations on GPUs for an input OpenCL kernel. The method is based on two models for speedup and normalized energy predictions over the default frequency configuration. Those are later combined into a multi-objective approach that predicts a Pareto-set of frequency configurations. Results show that our approach is very accurate at predicting extema and the Pareto set, and finds frequency configurations that dominate the default configuration in either energy or performance.DFG, 360291326, CELERITY: Innovative Modellierung für Skalierbare Verteilte Laufzeitsystem

Resiliency in numerical algorithm design for extreme scale simulations

This work is based on the seminar titled ‘Resiliency in Numerical Algorithm Design for Extreme Scale Simulations’ held March 1–6, 2020, at Schloss Dagstuhl, that was attended by all the authors. Advanced supercomputing is characterized by very high computation speeds at the cost of involving an enormous amount of resources and costs. A typical large-scale computation running for 48 h on a system consuming 20 MW, as predicted for exascale systems, would consume a million kWh, corresponding to about 100k Euro in energy cost for executing 1023 floating-point operations. It is clearly unacceptable to lose the whole computation if any of the several million parallel processes fails during the execution. Moreover, if a single operation suffers from a bit-flip error, should the whole computation be declared invalid? What about the notion of reproducibility itself: should this core paradigm of science be revised and refined for results that are obtained by large-scale simulation? Naive versions of conventional resilience techniques will not scale to the exascale regime: with a main memory footprint of tens of Petabytes, synchronously writing checkpoint data all the way to background storage at frequent intervals will create intolerable overheads in runtime and energy consumption. Forecasts show that the mean time between failures could be lower than the time to recover from such a checkpoint, so that large calculations at scale might not make any progress if robust alternatives are not investigated. More advanced resilience techniques must be devised. The key may lie in exploiting both advanced system features as well as specific application knowledge. Research will face two essential questions: (1) what are the reliability requirements for a particular computation and (2) how do we best design the algorithms and software to meet these requirements? While the analysis of use cases can help understand the particular reliability requirements, the construction of remedies is currently wide open. One avenue would be to refine and improve on system- or application-level checkpointing and rollback strategies in the case an error is detected. Developers might use fault notification interfaces and flexible runtime systems to respond to node failures in an application-dependent fashion. Novel numerical algorithms or more stochastic computational approaches may be required to meet accuracy requirements in the face of undetectable soft errors. These ideas constituted an essential topic of the seminar. The goal of this Dagstuhl Seminar was to bring together a diverse group of scientists with expertise in exascale computing to discuss novel ways to make applications resilient against detected and undetected faults. In particular, participants explored the role that algorithms and applications play in the holistic approach needed to tackle this challenge. This article gathers a broad range of perspectives on the role of algorithms, applications and systems in achieving resilience for extreme scale simulations. The ultimate goal is to spark novel ideas and encourage the development of concrete solutions for achieving such resilience holistically.Peer Reviewed"Article signat per 36 autors/es: Emmanuel Agullo, Mirco Altenbernd, Hartwig Anzt, Leonardo Bautista-Gomez, Tommaso Benacchio, Luca Bonaventura, Hans-Joachim Bungartz, Sanjay Chatterjee, Florina M. Ciorba, Nathan DeBardeleben, Daniel Drzisga, Sebastian Eibl, Christian Engelmann, Wilfried N. Gansterer, Luc Giraud, Dominik G ̈oddeke, Marco Heisig, Fabienne Jezequel, Nils Kohl, Xiaoye Sherry Li, Romain Lion, Miriam Mehl, Paul Mycek, Michael Obersteiner, Enrique S. Quintana-Ortiz, Francesco Rizzi, Ulrich Rude, Martin Schulz, Fred Fung, Robert Speck, Linda Stals, Keita Teranishi, Samuel Thibault, Dominik Thonnes, Andreas Wagner and Barbara Wohlmuth"Postprint (author's final draft

Mitigating the effects of vendor lock-in in edge cloud environments with open-source technologies

Cloud computing has been in the center of attention recently. Its popularity has increased significantly. More and more companies decide to use a cloud for running their applications. However, this introduces certain problems, such as vendor lock-in. Without a widely used standard, the systems become incompatible with each other. This thesis introduces a way to reduce the risk of vendor lock-in and uses open source technologies in order to make it available to as many people as possible. The explored solution is easy-to-use and light-weight compared to other ones. Furthermore, the use of certain technologies over others is suggested in the thesis to further reduce the risks of being locked to a single cloud provider

GME: GPU-based Microarchitectural Extensions to Accelerate Homomorphic Encryption

Fully Homomorphic Encryption (FHE) enables the processing of encrypted data without decrypting it. FHE has garnered significant attention over the past decade as it supports secure outsourcing of data processing to remote cloud services. Despite its promise of strong data privacy and security guarantees, FHE introduces a slowdown of up to five orders of magnitude as compared to the same computation using plaintext data. This overhead is presently a major barrier to the commercial adoption of FHE. In this work, we leverage GPUs to accelerate FHE, capitalizing on a well-established GPU ecosystem available in the cloud. We propose GME, which combines three key microarchitectural extensions along with a compile-time optimization to the current AMD CDNA GPU architecture. First, GME integrates a lightweight on-chip compute unit (CU)-side hierarchical interconnect to retain ciphertext in cache across FHE kernels, thus eliminating redundant memory transactions. Second, to tackle compute bottlenecks, GME introduces special MOD-units that provide native custom hardware support for modular reduction operations, one of the most commonly executed sets of operations in FHE. Third, by integrating the MOD-unit with our novel pipelined $64$-bit integer arithmetic cores (WMAC-units), GME further accelerates FHE workloads by $19\%$. Finally, we propose a Locality-Aware Block Scheduler (LABS) that exploits the temporal locality available in FHE primitive blocks. Incorporating these microarchitectural features and compiler optimizations, we create a synergistic approach achieving average speedups of $796\times$, $14.2\times$, and $2.3\times$ over Intel Xeon CPU, NVIDIA V100 GPU, and Xilinx FPGA implementations, respectively

GPGPU Reliability Analysis: From Applications to Large Scale Systems

Over the past decade, GPUs have become an integral part of mainstream high-performance computing (HPC) facilities. Since applications running on HPC systems are usually long-running, any error or failure could result in significant loss in scientific productivity and system resources. Even worse, since HPC systems face severe resilience challenges as progressing towards exascale computing, it is imperative to develop a better understanding of the reliability of GPUs. This dissertation fills this gap by providing an understanding of the effects of soft errors on the entire system and on specific applications. To understand system-level reliability, a large-scale study on GPU soft errors in the field is conducted. The occurrences of GPU soft errors are linked to several temporal and spatial features, such as specific workloads, node location, temperature, and power consumption. Further, machine learning models are proposed to predict error occurrences on GPU nodes so as to proactively and dynamically turning on/off the costly error protection mechanisms based on prediction results. To understand the effects of soft errors at the application level, an effective fault-injection framework is designed aiming to understand the reliability and resilience characteristics of GPGPU applications. This framework is effective in terms of reducing the tremendous number of fault injection locations to a manageable size while still preserving remarkable accuracy. This framework is validated with both single-bit and multi-bit fault models for various GPGPU benchmarks. Lastly, taking advantage of the proposed fault-injection framework, this dissertation develops a hierarchical approach to understanding the error resilience characteristics of GPGPU applications at kernel, CTA, and warp levels. In addition, given that some corrupted application outputs due to soft errors may be acceptable, we present a use case to show how to enable low-overhead yet reliable GPU computing for GPGPU applications

Corporate influence and the academic computer science discipline. [4: CMU]

Prosopographical work on the four major centers for computer research in the United States has now been conducted, resulting in big questions about the independence of, so called, computer science