Joe Devietti
Associate Professor and Undergraduate Curriculum Chair, Computer & Information Science
Joseph Devietti
my email address: my last name at cis dot upenn dot edu
(215) 746-4223
Levine Hall 572
3330 Walnut Street
Philadelphia, PA 19104-3409

I work on making multiprocessors easier to program by leveraging changes in both computer architectures and parallel programming models.

I am looking for new PhD students interested in systems and computer architecture. If you are interested in these topics please apply to our PhD program and drop me an email as well.


In Fall 2022 I'm teaching CIS 6010: GPGPU Programming and Architecture.


I'm lucky to be working with the following great students:

Former students

  • Gautam Mohan (Master's 2020. First employment: Amazon)
  • Yuanfeng Peng (PhD 2019). First employment: Google
  • Nicholas Renner (Master's 2019, now a PhD student at NYU)
  • Nimit Singhania (PhD 2018, co-advised with Rajeev Alur). First employment: Google
  • Christian DeLozier (PhD 2018). First employment: Assistant Professor at United States Naval Academy
  • Kavya Lakshminarayanan (Master's 2018) First employment: Microsoft
  • Richard Zang (Master's 2018) First employment: Microsoft
  • Sana Kamboj (Master's 2017) First employment: Qualcomm
  • Ariel Eizenberg (Master's 2016) First employment: Government of Israel
  • Brooke Fugate (Master's 2015, co-advised with André DeHon)
  • Liang Luo (Master's 2015, then a PhD student at the University of Washington)
  • Akshitha Sriraman (Master's 2015, then a PhD student at the University of Michigan)

Recent Publications full list

Many of the paper links below use the ACM's Author-izer service, which tracks download statistics and provides a small kickback to various ACM Special Interest Groups for each download.

  • OCOLOS: Online COde Layout OptimizationSOCOLOS: Online COde Layout OptimizationS
    ACM IEEE International Symposium on Microarchitecture (MICRO '22), October 2022

    The processor front-end has become an increasingly important bottleneck in recent years due to growing application code footprints, particularly in data centers. First-level instruction caches and branch prediction engines have not been able to keep up with this code growth, leading to more front-end stalls and lower Instructions Per Cycle (IPC). Profile-guided optimizations performed by compilers represent a promising approach, as they rearrange code to maximize instruction cache locality and branch prediction efficiency along a relatively small number of hot code paths. However, these optimizations require continuous profiling and rebuilding of applications to ensure that the code layout matches the collected profiles. If an application’s code is frequently updated, it becomes challenging to map profiling data from a previous version onto the latest version, leading to ignored profiling data and missed optimization opportunities.

    In this paper, we propose OCOLOS, the first online code layout optimization system for unmodified applications written in unmanaged languages. OCOLOS allows profile-guided optimization to be performed on a running process, instead of being performed offline and requiring the application to be re-launched. By running online, profile data is always relevant to the current execution and always maps perfectly to the running code. OCOLOS demonstrates how to achieve robust online code replacement in complex multithreaded applications like MySQL and MongoDB, without requiring any application changes. Our experiments show that OCOLOS can accelerate MySQL by up to 1.41x, the Verilator hardware simulator by up to 2.20x, and a build of the Clang compiler by up to 1.14x.

  • Twig: Profile-Guided BTB Prefetching for Data Center ApplicationsTwig: Profile-Guided BTB Prefetching for Data Center Applications
    ACM IEEE International Symposium on Microarchitecture (MICRO '21), October 2021

    Modern data center applications have deep software stacks, with instruction footprints that are orders of magnitude larger than typical instruction cache (I-cache) sizes. To efficiently prefetch instructions into the I-cache despite large application footprints, modern server-class processors implement a decoupled frontend with Fetch Directed Instruction Prefetching (FDIP). In this work, we first characterize the limitations of a decoupled frontend processor with FDIP and find that FDIP suffers from significant Branch Target Buffer (BTB) misses. We also find that existing techniques (e.g., stream prefetchers and predecoders) are unable to mitigate these misses, as they rely on an incomplete understanding of a program’s branching behavior.

    To address the shortcomings of existing BTB prefetching techniques, we propose Twig, a novel profile-guided BTB prefetching mechanism. Twig analyzes a production binary’s execution profile to identify critical BTB misses and inject BTB prefetch instructions into code. Additionally, Twig coalesces multiple non-contiguous BTB prefetches to improve the BTB’s locality. Twig exposes these techniques via new BTB prefetch instructions. Since Twig prefetches BTB entries without modifying the underlying BTB organization, it is easy to adopt in modern processors. We study Twig’s behavior across nine widely-used data center applications, and demonstrate that it achieves an average 20.86% (up to 145%) performance speedup over a baseline 8K-entry BTB, outperforming the state-of-the-art BTB prefetch mechanism by 19.82% (on average).

  • Ripple: Profile-Guided Instruction Cache Replacement for Data Center ApplicationsRipple: Profile-Guided Instruction Cache Replacement for Data Center Applications
    International Symposium on Computer Architecture (ISCA '21), June 2021

    Modern data center applications exhibit deep software stacks yielding large instruction footprints that frequently lead to instruction cache misses degrading performance, cost-efficiency, and energy efficiency. Although numerous mechanisms have been proposed to mitigate instruction cache misses, they still fall short of ideal cache behavior, and furthermore, introduce significant hardware overheads. We first investigate why existing I-cache miss mitigation mechanisms achieve sub-optimal performance for data center applications. We find that widely-studied instruction prefetchers fall short due to wasteful prefetch-induced evictions that are not handled by existing replacement policies. Alas, existing replacement policies are unable to mitigate wasteful evictions since they lack complete knowledge of a data center application’s complex program behavior.

    To make existing replacement policies aware of these eviction-inducing program behaviors, we propose Ripple, a novel software-only technique that profiles programs and uses program context to inform the underlying replacement policy about efficient replacement decisions. Ripple carefully identifies program contexts that lead to I-cache misses and sparingly injects “cache line eviction” instructions in suitable program locations at link time. We evaluate Ripple using nine popular data center applications and demonstrate that Ripple enables any replacement policy to achieve speedup that is closer to that of an ideal I-cache. Specifically, Ripple achieves an average performance improvement of 1.6% (up to 2.13%) due to a mean 19% (up to 28.6%) I-cache miss reduction.

  • Static detection of uncoalesced accesses in GPU programsStatic detection of uncoalesced accesses in GPU programs
    Formal Methods in System Design, March 2021

    GPU programming has become popular due to the high computational capabilities of GPUs. Obtaining significant performance gains with GPU is however challenging and the programmer needs to be aware of various subtleties of the GPU architecture. One such subtlety lies in accessing GPU memory, where certain access patterns can lead to poor performance. Such access patterns are referred to as uncoalesced global memory accesses. This work presents a light-weight compile-time static analysis to identify such accesses in GPU programs. The analysis relies on a novel abstraction which tracks the access pattern across multiple threads. The abstraction enables quick prediction while providing correctness guarantees. We have implemented the analysis in LLVM and compare it against a dynamic analysis implementation. The static analysis identifies 95 pre-existing uncoalesced accesses in Rodinia, a popular benchmark suite of GPU programs, and finishes within seconds for most programs, in comparison to the dynamic analysis which finds 69 accesses and takes orders of magnitude longer to finish.

  • Anytime Computation and Control for Autonomous SystemsAnytime Computation and Control for Autonomous Systems
    IEEE Transactions on Control Systems Technology, March 2021
    The correct and timely completion of the sensing and action loop is of utmost importance in safety critical autonomous systems. Crucial to the performance of this feedback control loop are the computation time and accuracy of the estimator which produces state estimates used by the controller. These state estimators often use computationally expensive perception algorithms like visual feature tracking. With on-board computers on autonomous robots being computationally limited, the computation time of such an estimation algorithm can at times be high enough to result in poor control performance. We develop a framework for codesign of anytime estimation and robust control algorithms, taking into account computation delays and estimation inaccuracies. This is achieved by constructing an anytime estimator from an off-the-shelf perception-based estimation algorithm and obtaining a trade-off curve for its computation time versus estimation error. This is used in the design of a robust predictive control algorithm that at run-time decides a contract, or operation mode, for the estimator in addition to controlling the dynamical system to meet its control objectives at a reduced computation energy cost. This codesign provides a mechanism through which the controller can use the tradeoff curve to reduce estimation delay at the cost of higher inaccuracy, while guaranteeing satisfaction of control objectives. Experiments on a hexrotor platform running a visual-based algorithm for state estimation show how our method results in up to a 10% improvement in control performance while simultaneously saving 5%-6% in computation energy as compared to a method that does not leverage the codesign.