Naila Farooqui

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In this paper we present the design and implementation of a dynamic instrumentation infrastructure for PTX programs that procedurally transforms kernels and manages related data structures. We show how performing instrumentation within the GPU Ocelot dynamic compiler infrastructure provides unique capabilities not available to other profiling and(More)
—As parallel execution platforms continue to proliferate , there is a growing need for real-time introspection tools to provide insight into platform behavior for performance debugging, correctness checks, and to drive effective resource management schemes. To address this need, we present the Lynx dynamic instrumentation system. Lynx provides the(More)
Dynamic instrumentation of GPGPU binaries makes possible real-time introspection methods for performance debugging, correctness checks, workload characterization, and runtime optimization. Such instrumentation involves inserting code at the instruction level of an application, while the application is running, thereby able to accurately profile(More)
Parallel architectures like GPUs are a tantalizing compute fabric for performance-hungry developers. While GPUs enable order-of-magnitude performance increases in many data-parallel application domains, writing efficient codes that can actually manifest those increases is a non-trivial endeavor, typically requiring developers to exercise specialized(More)
—Moving toward exascale, the number of GPUs in HPC machines is bound to increase, and applications will spend increasing amounts of time running on those GPU devices. While GPU usage has already led to substantial speedup for HPC codes, their failure rates due to overheating are at least 10 times higher than those seen for the CPUs now commonly used on HPC(More)
Recent integrated CPU-GPU processors like Intel's Broadwell and AMD's Kaveri support hardware CPU-GPU shared virtual memory, atomic operations, and memory coherency. This enables fine-grained CPU-GPU work-stealing, but architectural differences between the CPU and GPU hurt the performance of traditionally-implemented work-stealing on such processors. These(More)
High-end computing systems are becoming increasingly heterogeneous, with nodes comprised of multiple CPUs and accelerators, like GPGPUs, and with potential additional heterogeneity in memory configurations and network connectivities. Further, as we move to exascale systems, the view of their future use is one in which simulations co-run with online(More)
Energy efficiency is now a top design goal for all computing systems, from fitness trackers and tablets, where it affects battery life, to cloud computing centers, where it directly impacts operational cost, maintainability, and environmental impact. Today's widespread integrated CPU-GPU processors combine a CPU and a GPU compute device with different(More)
Over a distinguished career, Regents Professor Karsten Schwan has made significant contributions across a diverse array of topics in computer systems, including operating systems for multi-core platforms, virtualization technologies, enterprise middleware, and high-performance computing. In this paper, we summarize his legacy of key research contributions(More)