Researcher:
Akhtar, Palwisha

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Master Student

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Palwisha

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Akhtar

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Akhtar, Palwisha

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2019 - 201912020 - 20222

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Researcher Of Publications: search.filters.isAuthorOfPublication.9e0d0d38-0f00-4175-bf20-f729285f52c0

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    ComScribe: identifying intra-node GPU communication
    (Springer Science and Business Media Deutschland GmbH, 2021) N/A; N/A; N/A; Department of Computer Engineering; Akhtar, Palwisha; Tezcan, Erhan; Qararyah, Fareed Mohammad; Erten, Didem Unat; Master Student; Master Student; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; N/A; 219274
    GPU communication plays a critical role in performance and scalability of multi-GPU accelerated applications. With the ever increasing methods and types of communication, it is often hard for the programmer to know the exact amount and type of communication taking place in an application. Though there are prior works that detect communication in distributed systems for MPI and multi-threaded applications on shared memory systems, to our knowledge, none of these works identify intra-node GPU communication. We propose a tool, ComScribe that identifies and categorizes types of communication among all GPU-GPU and CPU-GPU pairs in a node. Built on top of NVIDIA’s profiler nvprof, ComScribe visualizes data movement as a communication matrix or bar-chart for explicit communication primitives, Unified Memory operations, and Zero-copy Memory transfers. To validate our tool on 16 GPUs, we present communication patterns of 8 micro- and 3 macro-benchmarks from NVIDIA, Comm|Scope, and MGBench benchmark suites. To demonstrate tool’s capabilities in real-life applications, we also present insightful communication matrices of two deep neural network models. All in all, ComScribe can guide the programmer in identifying which groups of GPUs communicate in what volume by using which primitives. This offers avenues to detect performance bottlenecks and more importantly communication bugs in an application. © 2021, Springer Nature Switzerland AG.
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    Monitoring collective communication among GPUs
    (Springer International Publishing Ag, 2022) N/A; N/A; N/A; N/A; Department of Computer Engineering; Soytürk, Muhammet Abdullah; Akhtar, Palwisha; Tezcan, Erhan; Erten, Didem Unat; PhD Student; Master Student; Master Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; N/A; 219274
    Communication among devices in multi-GPU systems plays an important role in terms of performance and scalability. In order to optimize an application, programmers need to know the type and amount of the communication happening among GPUs. Although there are prior works to gather this information in MPI applications on distributed systems and multi-threaded applications on shared memory systems, there is no tool that identifies communication among GPUs. Our prior work, CoMSCRIBE, presents a point-to-point (P2P) communication detection tool for GPUs sharing a common host. In this work, we extend CoMSCRIBE to identify communication among GPUs for collective and P2P communication primitives in NVIDIA's NCCL library. In addition to P2P communications, collective communications are commonly used in HPC and AI workloads thus it is important to monitor the induced data movement due to collectives. Our tool extracts the size and the frequency of data transfers in an application and visualizes them as a communication matrix. To demonstrate the tool in action, we present communication matrices and some statistics for two applications coming from machine translation and image classification domains.
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    PublicationOpen Access
    ComDetective: a lightweight communication detection tool for threads
    (Association for Computing Machinery (ACM), 2019) Chabbi, Milind; N/A; Department of Computer Engineering; Sasongko, Muhammad Aditya; Akhtar, Palwisha; Erten, Didem Unat; Researcher; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 219274
    Inter-thread communication is a vital performance indicator in shared-memory systems. Prior works on identifying inter-thread communication employed hardware simulators or binary instrumentation and suffered from inaccuracy or high overheads-both space and time-making them impractical for production use. We propose COMDETECTIVE, which produces communication matrices that are accurate and introduces low runtime and low memory overheads, thus making it practical for production use. COMDETECTIVE employs hardware performance counters to sample memory-access events and uses hardware debug registers to sample communicating pairs of threads. COMDETECTIVE can differentiate communication as true or false sharing between threads. Its runtime and memory overheads are only 1.30x and 1.27x, respectively, for the 18 applications studied under 500K sampling period. Using COMDETECTIVE, we produce insightful communication matrices for microbenchmarks, PARSEC benchmark suite, and several CORAL applications and compare the generated matrices against MPI counterparts. Guided by COMDETECTIVE, we optimize a few codes and achieve up to 13% speedup.