Karthik Pattabiraman

Bio: Karthik Pattabiraman is an academic researcher from University of British Columbia. The author has contributed to research in topics: Fault injection & JavaScript. The author has an hindex of 33, co-authored 138 publications receiving 3637 citations. Previous affiliations of Karthik Pattabiraman include University of Illinois at Urbana–Champaign & Microsoft.

Flikker: saving DRAM refresh-power through critical data partitioning

Abstract: Energy has become a first-class design constraint in computer systems. Memory is a significant contributor to total system power. This paper introduces Flikker, an application-level technique to reduce refresh power in DRAM memories. Flikker enables developers to specify critical and non-critical data in programs and the runtime system allocates this data in separate parts of memory. The portion of memory containing critical data is refreshed at the regular refresh-rate, while the portion containing non-critical data is refreshed at substantially lower rates. This partitioning saves energy at the cost of a modest increase in data corruption in the non-critical data. Flikker thus exposes and leverages an interesting trade-off between energy consumption and hardware correctness. We show that many applications are naturally tolerant to errors in the non-critical data, and in the vast majority of cases, the errors have little or no impact on the application's final outcome. We also find that Flikker can save between 20-25% of the power consumed by the memory sub-system in a mobile device, with negligible impact on application performance. Flikker is implemented almost entirely in software, and requires only modest changes to the hardware.

Understanding error propagation in deep learning neural network (DNN) accelerators and applications

Abstract: Deep learning neural networks (DNNs) have been successful in solving a wide range of machine learning problems. Specialized hardware accelerators have been proposed to accelerate the execution of DNN algorithms for high-performance and energy efficiency. Recently, they have been deployed in datacenters (potentially for business-critical or industrial applications) and safety-critical systems such as self-driving cars. Soft errors caused by high-energy particles have been increasing in hardware systems, and these can lead to catastrophic failures in DNN systems. Traditional methods for building resilient systems, e.g., Triple Modular Redundancy (TMR), are agnostic of the DNN algorithm and the DNN accelerator's architecture. Hence, these traditional resilience approaches incur high overheads, which makes them challenging to deploy. In this paper, we experimentally evaluate the resilience characteristics of DNN systems (i.e., DNN software running on specialized accelerators). We find that the error resilience of a DNN system depends on the data types, values, data reuses, and types of layers in the design. Based on our observations, we propose two efficient protection techniques for DNN systems.

Quantifying the Accuracy of High-Level Fault Injection Techniques for Hardware Faults

Abstract: Hardware errors are on the rise with reducing feature sizes, however tolerating them in hardware is expensive Researchers have explored software-based techniques for building error resilient applications Many of these techniques leverage application-specific resilience characteristics to keep overheads low Understanding application-specific resilience characteristics requires software fault-injection mechanisms that are both accurate and capable of operating at a high-level of abstraction to allow developers to reason about error resilience In this paper, we quantify the accuracy of high-level software fault injection mechanisms vis-a-vis those that operate at the assembly or machine code levels To represent high-level injection mechanisms, we built a fault injector tool based on the LLVM compiler, called LLFI LLFI performs fault injection at the LLVM intermediate code level of the application, which is close to the source code We quantitatively evaluate the accuracy of LLFI with respect to assembly level fault injection, and understand the reasons for the differences

Mining questions asked by web developers

Abstract: Modern web applications consist of a significant amount of client- side code, written in JavaScript, HTML, and CSS. In this paper, we present a study of common challenges and misconceptions among web developers, by mining related questions asked on Stack Over- flow. We use unsupervised learning to categorize the mined questions and define a ranking algorithm to rank all the Stack Overflow questions based on their importance. We analyze the top 50 questions qualitatively. The results indicate that (1) the overall share of web development related discussions is increasing among developers, (2) browser related discussions are prevalent; however, this share is decreasing with time, (3) form validation and other DOM related discussions have been discussed consistently over time, (4) web related discussions are becoming more prevalent in mobile development, and (5) developers face implementation issues with new HTML5 features such as Canvas. We examine the implications of the results on the development, research, and standardization communities.

GPU-Qin: A methodology for evaluating the error resilience of GPGPU applications

Abstract: While graphics processing units (GPUs) have gained wide adoption as accelerators for general-purpose applications (GPGPU), the end-to-end reliability implications of their use have not been quantified. Fault injection is a widely used method for evaluating the reliability of applications. However, building a fault injector for GPGPU applications is challenging due to their massive parallelism, which makes it difficult to achieve representativeness while being time-efficient. This paper makes three key contributions. First, it presents the design of a fault-injection methodology to evaluate end-to-end reliability properties of application kernels running on GPUs. Second, it introduces a fault-injection tool that uses real GPU hardware and offers a good balance between the representativeness and the efficiency of the fault injection experiments. Third, this paper characterizes the error resilience characteristics of twelve GPGPU applications.

The C programming language

Abstract: This ebook is the first authorized digital version of Kernighan and Ritchie's 1988 classic, The C Programming Language (2nd Ed.). One of the best-selling programming books published in the last fifty years, "K&R" has been called everything from the "bible" to "a landmark in computer science" and it has influenced generations of programmers. Available now for all leading ebook platforms, this concise and beautifully written text is a "must-have" reference for every serious programmers digital library. As modestly described by the authors in the Preface to the First Edition, this "is not an introductory programming manual; it assumes some familiarity with basic programming concepts like variables, assignment statements, loops, and functions. Nonetheless, a novice programmer should be able to read along and pick up the language, although access to a more knowledgeable colleague will help."

A Survey of Techniques for Approximate Computing

Abstract: Approximate computing trades off computation quality with effort expended, and as rising performance demands confront plateauing resource budgets, approximate computing has become not merely attractive, but even imperative. In this article, we present a survey of techniques for approximate computing (AC). We discuss strategies for finding approximable program portions and monitoring output quality, techniques for using AC in different processing units (e.g., CPU, GPU, and FPGA), processor components, memory technologies, and so forth, as well as programming frameworks for AC. We classify these techniques based on several key characteristics to emphasize their similarities and differences. The aim of this article is to provide insights to researchers into working of AC techniques and inspire more efforts in this area to make AC the mainstream computing approach in future systems.

[서평]「Computer Organization and Design, The Hardware/Software Interface」

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EnerJ: approximate data types for safe and general low-power computation

Abstract: Energy is increasingly a first-order concern in computer systems. Exploiting energy-accuracy trade-offs is an attractive choice in applications that can tolerate inaccuracies. Recent work has explored exposing this trade-off in programming models. A key challenge, though, is how to isolate parts of the program that must be precise from those that can be approximated so that a program functions correctly even as quality of service degrades.We propose using type qualifiers to declare data that may be subject to approximate computation. Using these types, the system automatically maps approximate variables to low-power storage, uses low-power operations, and even applies more energy-efficient algorithms provided by the programmer. In addition, the system can statically guarantee isolation of the precise program component from the approximate component. This allows a programmer to control explicitly how information flows from approximate data to precise data. Importantly, employing static analysis eliminates the need for dynamic checks, further improving energy savings. As a proof of concept, we develop EnerJ, an extension to Java that adds approximate data types. We also propose a hardware architecture that offers explicit approximate storage and computation. We port several applications to EnerJ and show that our extensions are expressive and effective; a small number of annotations lead to significant potential energy savings (10%-50%) at very little accuracy cost.

Online Tracking: A 1-million-site Measurement and Analysis

Abstract: We present the largest and most detailed measurement of online tracking conducted to date, based on a crawl of the top 1 million websites. We make 15 types of measurements on each site, including stateful (cookie-based) and stateless (fingerprinting-based) tracking, the effect of browser privacy tools, and the exchange of tracking data between different sites ("cookie syncing"). Our findings include multiple sophisticated fingerprinting techniques never before measured in the wild. This measurement is made possible by our open-source web privacy measurement tool, OpenWPM, which uses an automated version of a full-fledged consumer browser. It supports parallelism for speed and scale, automatic recovery from failures of the underlying browser, and comprehensive browser instrumentation. We demonstrate our platform's strength in enabling researchers to rapidly detect, quantify, and characterize emerging online tracking behaviors.