Search Results - "Ahmad, Masab"

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  1. 1

    A performance predictor for implementation selection of parallelized static and temporal graph algorithms by Rehman, Akif, Ahmad, Masab, Khan, Omer

    Published in Concurrency and computation (25-01-2022)
    “…Task‐based execution of graph workloads allows various ordered and unordered implementations, with tasks representing dependencies between graph vertices and…”
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    Journal Article
  2. 2

    Advancing the State-of-the-Art in Hardware Trojans Detection by Haider, Syed Kamran, Jin, Chenglu, Ahmad, Masab, Shila, Devu Manikantan, Khan, Omer, van Dijk, Marten

    “…Over the past decade, Hardware Trojans (HTs) research community has made significant progress towards developing effective countermeasures for various types of…”
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    Journal Article
  3. 3

    In-Hardware Moving Compute to Data Model to Accelerate Thread Synchronization on Large Multicores by Ahmad, Masab, Dogan, Halit, Joao, Jose A., Khan, Omer

    Published in IEEE MICRO (01-01-2020)
    “…In this article, the moving computation to data model (MC2D) is proposed to accelerate thread synchronization by pinning shared data to dedicated cores, and…”
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    Journal Article
  4. 4

    Efficient Situational Scheduling of Graph Workloads on Single-Chip Multicores and GPUs by Ahmad, Masab, Michael, Christopher J., Khan, Omer

    Published in IEEE MICRO (01-01-2017)
    “…Situational dynamic changes in graph analytic algorithm implementations give rise to efficiency challenges in concurrent hardware, such as GPUs and large-scale…”
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    Journal Article
  5. 5

    GraphTuner: An Input Dependence Aware Loop Perforation Scheme for Efficient Execution of Approximated Graph Algorithms by Omar, Hamza, Ahmad, Masab, Khan, Omer

    “…Graph algorithms have gained popularity and are utilized in high performance and mobile computing paradigms. Input dependence due to input graph changes leads…”
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    Conference Proceeding
  6. 6

    Accelerating Graph Processing on Large-Scale Multicores by Ahmad, Masab

    Published 01-01-2019
    “…With the ever-increasing amount of data and input variations, portable performance is becoming harder to exploit on today’s architectures. Computational setups…”
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    Dissertation
  7. 7

    POSTER: Exploiting Multi-Level Task Dependencies to Prune Redundant Work in Relax-Ordered Task-Parallel Algorithms by Ahmad, Masab, Shan, Mohsin, Rehman, Akif, Khan, Omer

    “…Work-efficient task-parallel algorithms enforce ordering between tasks using queuing primitives. Such algorithms offer limited parallelism due to queuing…”
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    Conference Proceeding
  8. 8

    Understanding Concurrency for Graph Workloads in Large Scale Multicores by Ahmad, Masab

    Published 01-01-2016
    “…Algorithms operating on a graph setting are known to be highly irregular and un- structured. This leads to workload imbalance and data locality challenge when…”
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    Dissertation
  9. 9

    Accelerating Synchronization in Graph Analytics Using Moving Compute to Data Model on Tilera TILE-Gx72 by Dogan, Halit, Ahmad, Masab, Joao, Jose, Khan, Omer

    “…The shared memory cache coherence paradigm is prevalent in modern multicores. However, as the number of cores increases, synchronization between threads limits…”
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    Conference Proceeding
  10. 10

    Power & throughput optimized lifting architecture for Wavelet Packet Transform by Ahmad, Masab, Kamboh, Awais Mehmood, Hafiz, Rehan

    “…This paper presents area-power efficient architectures for the lifting based Wavelet Packet Transform (WPT). Using Daubechies 6 as an example, three different…”
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    Conference Proceeding
  11. 11

    CRONO: A Benchmark Suite for Multithreaded Graph Algorithms Executing on Futuristic Multicores by Ahmad, Masab, Hijaz, Farrukh, Qingchuan Shi, Khan, Omer

    “…Algorithms operating on a graph setting are known to be highly irregular and unstructured. This leads to workload imbalance and data locality challenge when…”
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    Conference Proceeding
  12. 12

    GPU concurrency choices in graph analytics by Ahmad, Masab, Khan, Omer

    “…Graph analytics is becoming ever more ubiquitous in today's world. However, situational dynamic changes in input graphs, such as changes in traffic and weather…”
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    Conference Proceeding
  13. 13

    HeteroMap: A Runtime Performance Predictor for Efficient Processing of Graph Analytics on Heterogeneous Multi-Accelerators by Ahmad, Masab, Dogan, Halit, Michael, Christopher J., Khan, Omer

    “…With the ever-increasing amount of data and input variations, portable performance is becoming harder to exploit on today's architectures. Computational setups…”
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    Conference Proceeding
  14. 14

    Accelerating Graph and Machine Learning Workloads Using a Shared Memory Multicore Architecture with Auxiliary Support for In-hardware Explicit Messaging by Dogan, Halit, Hijaz, Farrukh, Ahmad, Masab, Kahne, Brian, Wilson, Peter, Khan, Omer

    “…Shared Memory stands out as a sine qua non for parallel programming of many commercial and emerging multicore processors. It optimizes patterns of…”
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    Conference Proceeding
  15. 15

    Efficient parallelization of path planning workload on single-chip shared-memory multicores by Ahmad, Masab, Lakshminarasimhan, Kartik, Khan, Omer

    “…Path planning problems greatly arise in many applications where the objective is to find the shortest path from a given source to destination. In this paper,…”
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    Conference Proceeding
  16. 16

    Software-Hardware Managed Last-level Cache Allocation Scheme for Large-Scale NVRAM-Based Multicores Executing Parallel Data Analytics Applications by Ahmad, Masab, Dogan, Halit, Checconi, Fabio, Que, Xinyu, Buono, Daniele, Khan, Omer

    “…Developments in machine learning and graph analytics have seen these fields establish themselves as pervasive in a wide range of applications. Non-volatile…”
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    Conference Proceeding
  17. 17

    M-MAP: Multi-factor memory authentication for secure embedded processors by Haider, Syed Kamran, Ahmad, Masab, Hijaz, Farrukh, Patni, Astha, Johnson, Ethan, Seita, Matthew, Khan, Omer, van Dijk, Marten

    “…The challenges faced in securing embedded computing systems against multifaceted memory safety vulnerabilities have prompted great interest in the development…”
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    Conference Proceeding