ASPIRE Seminar: Mark Silberstein, UT-Austin, Tues Feb 5 at 3:30pm in 380 Soda
Speaker: Mark Silberstein, UT-Austin
Title: Operating System Support for High-Throughput Processors
Abstract: The processor landscape has fractured into latency-optimized CPUs, throughput-oriented GPUs, and soon, custom accelerators. Future applications will need to cohesively use a variety of hardware to achieve their performance and power goals. However building efficient systems that use accelerators today is incredibly difficult.
In this talk we will argue that the root cause of this complexity lies in the lack of adequate operating system support for accelerators.
While operating systems provide optimized resource management and Input/Output (I/O) services for CPU applications, they make no such services available to accelerator programs.
We propose GPUfs - an operating system layer which enables access to files directly from programs running on throughput-oriented accelerators, such as GPUs. GPUfs extends the constrained GPU-as-coprocessor programming model, turning GPUs into first-class computing devices with full file I/O support. It provides a POSIX-like API for GPU programs, exploits parallelism for efficiency, and optimizes for access locality by extending a CPU buffer cache into physical memories of all GPUs in a single machine.
Using real benchmarks we show that GPUfs simplifies the development of efficient applications by eliminating the GPU management complexity, and broadens the range of applications that can be accelerated by GPUs. For example, a simple self-contained GPU program which searches for a set of strings in the entire tree of Linux kernel source files completes in about third of the time of an 8-CPU-core run.
Joint work with Idit Keidar (Technion), Bryan Ford (Yale) and Emmett Witchel (UT Austin)
Bio: Mark Silberstein is a postdoctoral fellow in the Operating Systems and Architecture group at the University of Texas at Austin. He holds a PhD in Computer Science from the Technion. His thesis focused on parallel algorithms and resource management in high-performance large-scale distributed systems. His research in GPU computing includes acceleration of memory-intensive applications via software-managed caching, power-efficient resource allocation in CPU-GPU hybrids, hard real-time stream processing, operating system abstractions and file I/O services for GPUs.
More information: https://sites.google.com/site/silbersteinmark.