[os-reading] Fwd: Talk today on OS support for GPUs


Date: Mon, 4 Feb 2013 11:52:17 -0600
From: Vijay Chidambaram <vvijay03@xxxxxxxxx>
Subject: [os-reading] Fwd: Talk today on OS support for GPUs
We discussed GPUfs last Wednesday. Looks like he will be talking about it today. People who had questions, you can ask him directly! 

---------- Forwarded message ----------
From: Michael Swift <swift@xxxxxxxxxxx>
Date: Mon, Feb 4, 2013 at 11:40 AM
Subject: Talk today on OS support for GPUs
To: graduates@xxxxxxxxxxx, faculty@xxxxxxxxxxx


Hi folks -

Mark Silberstein from the University of Texas will be talking at 4 pm in room 2310 about operating system support for GPUs.

- Mike

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 wwill 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 to 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, Israel. His thesis focused on parallel algorithms and resource management in high-performance large-scale distributed systems. His research in GPU computing includes software-managed caching and memory-intensive applications, power efficient and hard real-time scheduling in CPU-GPU hybrids, GPUs and OS privacy, operating system abstractions and I/O services for GPUs. More information:https://sites.google.com/site/silbersteinmark


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