Ivan Fernandez

ETH Zurich

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Juan Gómez-Luna

ETH Zurich

Onur Mutlu

ETH Zurich

Christina Giannoula

Vector Institute

ÓP

Óscar Plata

Academia Nacional de Ciencias de Bolivia

Eladio Gutiérrez

Universidad de Málaga

Sudip Mittal

Mississippi State University

Shahram Rahimi

Mississippi State University

Ricardo Quislant

Universidad de Málaga

Subash Neupane

Mississippi State University

Nectarios Koziris

National Technical University of Athens

Cooperative Institutions

Universidad de Málaga

ETH Zurich

Mississippi State University

University of Toronto

Universitat Politècnica de Catalunya

University of Maryland, Baltimore County

University of Illinois Urbana-Champaign

Carnegie Mellon University

Barcelona Supercomputing Center

Vector Institute

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Comparing assembly strategies for third-generation sequencing technologies across different genomes

Genomics (2023)

Elena Espinosa Rocío Bautista Ivan Fernandez Rafael Larrosa Jiménez Emilio L. Zapata

The recent advent of long-read sequencing technologies, such as Pacific Biosciences (PacBio) and Oxford Nanopore technology (ONT), has led to substantial accuracy and computational cost improvements. However, de novo whole-genome assembly still presents significant challenges related to the computational cost and the quality of the results. Accordingly, sequencing accuracy and throughput continue to improve, and many tools are constantly emerging. Therefore, selecting the correct sequencing platform, the proper sequencing depth and the assembly tools are necessary to perform high-quality assembly. This paper evaluates the primary assembly reconstruction from recent hybrid and non-hybrid pipelines on different genomes. We find that using PacBio high-fidelity long-read (HiFi) plays an essential role in haplotype construction with respect to ONT reads. However, we observe a substantial improvement in the correctness of the assembly from high-fidelity ONT datasets and combining it with HiFi or short-reads.

Sequence assembly

Hybrid genome assembly

10.1016/j.ygeno.2023.110700

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Citations (5)

Time Series Analysis Using Transprecision Computing

Ivan Fernandez

Source

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Towards Efficient Sparse Matrix Vector Multiplication on Real Processing-In-Memory Architectures

Christina Giannoula Ivan Fernandez Juan Gómez-Luna Nectarios Koziris Georgios Goumas

Several manufacturers have already started to commercialize near-bank Processing-In-Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures place simple cores close to DRAM banks. Recent research demonstrates that they can yield significant performance and energy improvements in parallel applications by alleviating data access costs. Real PIM systems can provide high levels of parallelism, large aggregate memory bandwidth and low memory access latency, thereby being a good fit to accelerate the Sparse Matrix Vector Multiplication (SpMV) kernel. SpMV has been characterized as one of the most significant and thoroughly studied scientific computation kernels. It is primarily a memory-bound kernel with intensive memory accesses due its algorithmic nature, the compressed matrix format used, and the sparsity patterns of the input matrices given. This paper provides the first comprehensive analysis of SpMV on a real-world PIM architecture, and presents SparseP, the first SpMV library for real PIM architectures. We make two key contributions. First, we design efficient SpMV algorithms to accelerate the SpMV kernel in current and future PIM systems, while covering a wide variety of sparse matrices with diverse sparsity patterns. Second, we provide the first comprehensive analysis of SpMV on a real PIM architecture. Specifically, we conduct our rigorous experimental analysis of SpMV kernels in the UPMEM PIM system, the first publicly-available real-world PIM architecture. Our extensive evaluation provides new insights and recommendations for software designers and hardware architects to efficiently accelerate the SpMV kernel on real PIM systems. For more information about our thorough characterization on the SpMV PIM execution, results, insights and the open-source SparseP software package [21], we refer the reader to the full version of the paper [3, 4]. The SparseP software package is publicly and freely available at https://github.com/CMU-SAFARI/SparseP.

Kernel (algebra)

Memory bandwidth

10.1145/3489048.3522661

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Citations (6)

Energy-Efficient Time Series Analysis Using Transprecision Computing

Ivan Fernandez Ricardo Quislant Eladio Gutiérrez Óscar Plata

Time series analysis is a key step in monitoring and predicting events over time in domains such as epidemiology, genomics, medicine, seismology, speech recognition, and economics. Matrix Profile has been recently proposed as a promising technique to perform time series analysis. For each subsequence, the matrix profile provides the most similar neighbour in the time series. This computation requires a huge amount of floating-point (FP) operations, which are a major contributor (approximately 50%) to the energy consumption in modern computing platforms. Transprecision Computing has recently emerged as a promising approach to improve energy efficiency and performance by tolerating some loss of precision in FP operations. In this work, we study how the matrix profile parallel algorithms benefit from transprecision computing using a recently proposed transprecision FPU. This FPU is intended to be integrated on embedded devices as part of RISC-V processors, FPGAs or ASICs to perform energy-efficient time series analysis. To this end, we propose an accuracy metric to compare the results with the double precision matrix profile. We use this metric to explore a wide range of exponent and mantissa combinations for a variety of datasets, as well as a mixed precision and a vectorized approach. Our analysis reveals that the energy consumption is reduced up to 3.3x compared with double precision approaches, while only slightly affecting the accuracy.

Matrix (chemical analysis)

10.1109/sbac-pad49847.2020.00022

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MATSA: An MRAM-Based Energy-Efficient Accelerator for Time Series Analysis

IEEE Access (2024)

Ivan Fernandez Christina Giannoula Aditya Manglik Ricardo Quislant Nika Mansouri Ghiasi

Time Series Analysis (TSA) is a critical workload to extract valuable information from collections of sequential data, e.g., detecting anomalies in electrocardiograms. Subsequence Dynamic Time Warping (sDTW) is the state-of-the-art algorithm for high-accuracy TSA. We find that the performance and energy efficiency of sDTW on conventional CPU and GPU platforms are heavily burdened by the latency and energy overheads of data movement between the compute and the memory units. sDTW exhibits low arithmetic intensity and low data reuse on conventional platforms, stemming from poor amortization of the data movement overheads. To improve the performance and energy efficiency of the sDTW algorithm, we propose MATSA, the first Magnetoresistive RAM (MRAM)-based Accelerator for TSA. MATSA leverages Processing-Using-Memory (PUM) based on MRAM crossbars to minimize data movement overheads and exploit parallelism in sDTW. MATSA improves performance by 7.35×/6.15×/6.31× and energy efficiency by 11.29×/4.21×/2.65× over server-class CPU, GPU, and Processing-Near-Memory platforms, respectively.

Magnetoresistive random-access memory

10.1109/access.2024.3373311

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Citations (2)

A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels

Subash Neupane Ivan Fernandez Wilson Patterson Sudip Mittal Shahram Rahimi

A modern vehicle fitted with sensors, actuators, and Electronic Control Units (ECUs) can be divided into several operational subsystems called Functional Working Groups (FWGs). Examples of these FWGs include the engine system, transmission, fuel system, brakes, etc. Each FWG has associated sensor-channels that gauge vehicular operating conditions. This data rich environment is conducive to the development of Predictive Maintenance (PdM) technologies. Undercutting various PdM technologies is the need for robust anomaly detection models that can identify events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal vehicular operational behavior. In this paper, we introduce the Vehicle Performance, Reliability, and Operations (VePRO) dataset and use it to create a multi-phased approach to anomaly detection. Utilizing Temporal Convolution Networks (TCN), our anomaly detection system can achieve 96% detection accuracy and accurately predicts 91% of true anomalies. The performance of our anomaly detection system improves when sensor channels from multiple FWGs are utilized.

Anomaly (physics)

Convolution (computer science)

10.1109/cic56439.2022.00024

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Citations (2)

DAMOV: A New Methodology and Benchmark Suite for Evaluating Data Movement Bottlenecks

arXiv (Cornell University) (2021)

Geraldo F. Oliveira Juan Gómez-Luna Lois Orosa Saugata Ghose Nandita Vijaykumar

Data movement between the CPU and main memory is a first-order obstacle against improving performance, scalability, and energy efficiency in modern systems. Computer systems employ a range of techniques to reduce overheads tied to data movement, spanning from traditional mechanisms (e.g., deep multi-level cache hierarchies, aggressive hardware prefetchers) to emerging techniques such as Near-Data Processing (NDP), where some computation is moved close to memory. Our goal is to methodically identify potential sources of data movement over a broad set of applications and to comprehensively compare traditional compute-centric data movement mitigation techniques to more memory-centric techniques, thereby developing a rigorous understanding of the best techniques to mitigate each source of data movement. With this goal in mind, we perform the first large-scale characterization of a wide variety of applications, across a wide range of application domains, to identify fundamental program properties that lead to data movement to/from main memory. We develop the first systematic methodology to classify applications based on the sources contributing to data movement bottlenecks. From our large-scale characterization of 77K functions across 345 applications, we select 144 functions to form the first open-source benchmark suite (DAMOV) for main memory data movement studies. We select a diverse range of functions that (1) represent different types of data movement bottlenecks, and (2) come from a wide range of application domains. Using NDP as a case study, we identify new insights about the different data movement bottlenecks and use these insights to determine the most suitable data movement mitigation mechanism for a particular application. We open-source DAMOV and the complete source code for our new characterization methodology at this https URL.

Benchmark (surveying)

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DAMOV: A New Methodology and Benchmark Suite for Evaluating Data Movement Bottlenecks

IEEE Access (2021)

Geraldo F. Oliveira Juan Gómez-Luna Lois Orosa Saugata Ghose Nandita Vijaykumar

Data movement between the CPU and main memory is a first-order obstacle against improving performance, scalability, and energy efficiency in modern systems. Computer systems employ a range of techniques to reduce overheads tied to data movement, spanning from traditional mechanisms (e.g., deep multi-level cache hierarchies, aggressive hardware prefetchers) to emerging techniques such as Near-Data Processing (NDP), where some computation is moved close to memory. Prior NDP works investigate the root causes of data movement bottlenecks using different profiling methodologies and tools. However, there is still a lack of understanding about the key metrics that can identify different data movement bottlenecks and their relation to traditional and emerging data movement mitigation mechanisms. Our goal is to methodically identify potential sources of data movement over a broad set of applications and to comprehensively compare traditional compute-centric data movement mitigation techniques (e.g., caching and prefetching) to more memory-centric techniques (e.g., NDP), thereby developing a rigorous understanding of the best techniques to mitigate each source of data movement. With this goal in mind, we perform the first large-scale characterization of a wide variety of applications, across a wide range of application domains, to identify fundamental program properties that lead to data movement to/from main memory. We develop the first systematic methodology to classify applications based on the sources contributing to data movement bottlenecks. From our large-scale characterization of 77K functions across 345 applications, we select 144 functions to form the first open-source benchmark suite (DAMOV) for main memory data movement studies.We select a diverse range of functions that (1) represent different types of data movement bottlenecks, and (2) come from a wide range of application domains. Using NDP as a case study, we identify new insights about the different data movement bottlenecks and use these insights to determine the most suitable data movement mitigation mechanism for a particular application. We open-source DAMOV and the complete source code for our new characterization methodology at https: //github.com/CMU-SAFARI/DAMOV.

Benchmark (surveying)

Profiling (computer programming)

10.1109/access.2021.3110993

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Citations (50)

SynCron: Efficient Synchronization Support for Near-Data-Processing Architectures

Christina Giannoula Nandita Vijaykumar Νικέλα Παπαδοπούλου Vasileios Karakostas Ivan Fernandez

Near-Data-Processing (NDP) architectures present a promising way to alleviate data movement costs and can provide significant performance and energy benefits to parallel applications. Typically, NDP architectures support several NDP units, each including multiple simple cores placed close to memory. To fully leverage the benefits of NDP and achieve high performance for parallel workloads, efficient synchronization among the NDP cores of a system is necessary. However, supporting synchronization in many NDP systems is challenging because they lack shared caches and hardware cache coherence support, which are commonly used for synchronization in multicore systems, and communication across different NDP units can be expensive. This paper comprehensively examines the synchronization problem in NDP systems, and proposes SynCron, an end-to-end synchronization solution for NDP systems. SynCron adds low-cost hardware support near memory for synchronization acceleration, and avoids the need for hardware cache coherence support. SynCron has three components: 1) a specialized cache memory structure to avoid memory accesses for synchronization and minimize latency overheads, 2) a hierarchical message-passing communication protocol to minimize expensive communication across NDP units of the system, and 3) a hardware-only overflow management scheme to avoid performance degradation when hardware resources for synchronization tracking are exceeded. We evaluate SynCron using a variety of parallel workloads, covering various contention scenarios. SynCron improves performance by 1.27× on average (up to 1.78×) under high-contention scenarios, and by 1.35× on average (up to 2.29×) under low-contention real applications, compared to state-of-the-art approaches. SynCron reduces system energy consumption by 2.08× on average (up to 4.25×).

Cache coherence

Multi-core processor

Data synchronization

Leverage (statistics)

10.1109/hpca51647.2021.00031

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Citations (49)

Poison Attacks and Adversarial Prompts Against an Informed University Virtual Assistant

arXiv (Cornell University) (2024)

Ivan Fernandez Subash Neupane Sudip Mittal Shahram Rahimi

Recent research has shown that large language models (LLMs) are particularly vulnerable to adversarial attacks. Since the release of ChatGPT, various industries are adopting LLM-based chatbots and virtual assistants in their data workflows. The rapid development pace of AI-based systems is being driven by the potential of Generative AI (GenAI) to assist humans in decision making. The immense optimism behind GenAI often overshadows the adversarial risks associated with these technologies. A threat actor can use security gaps, poor safeguards, and limited data governance to carry out attacks that grant unauthorized access to the system and its data. As a proof-of-concept, we assess the performance of BarkPlug, the Mississippi State University chatbot, against data poison attacks from a red team perspective.

10.48550/arxiv.2412.06788

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