Commodity computer systems contain more and more processor cores and exhibit increasingly diverse architectural tradeoffs, including memory hierarchies, interconnects, instruction sets and variants, and IO configurations. Previous high-performance computing systems have scaled in specific cases, but the dynamic nature of modern client and server workloads, coupled with the impossibility of statically optimizing an OS for all workloads and hardware variants pose serious challenges for operating system structures.We argue that the challenge of future multicore hardware is best met by embracing the networked nature of the machine, rethinking OS architecture using ideas from distributed systems. We investigate a new OS structure, the multikernel, that treats the machine as a network of independent cores, assumes no inter-core sharing at the lowest level, and moves traditional OS functionality to a distributed system of processes that communicate via message-passing.We have implemented a multikernel OS to show that the approach is promising, and we describe how traditional scalability problems for operating systems (such as memory management) can be effectively recast using messages and can exploit insights from distributed systems and networking. An evaluation of our prototype on multicore systems shows that, even on present-day machines, the performance of a multikernel is comparable with a conventional OS, and can scale better to support future hardware.

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The multikernel: a new OS architecture for scalable multicore systems

Energy efficiency is the new fundamental limiter of processor performance, way beyond numbers of processors.

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The future of microprocessors

Recent advances in computing have led to an explosion in the amount of data being generated. Processing the ever-growing data in a timely manner has made throughput computing an important aspect for emerging applications. Our analysis of a set of important throughput computing kernels shows that there is an ample amount of parallelism in these kernels which makes them suitable for today's multi-core CPUs and GPUs. In the past few years there have been many studies claiming GPUs deliver substantial speedups (between 10X and 1000X) over multi-core CPUs on these kernels. To understand where such large performance difference comes from, we perform a rigorous performance analysis and find that after applying optimizations appropriate for both CPUs and GPUs the performance gap between an Nvidia GTX280 processor and the Intel Core i7-960 processor narrows to only 2.5x on average. In this paper, we discuss optimization techniques for both CPU and GPU, analyze what architecture features contributed to performance differences between the two architectures, and recommend a set of architectural features which provide significant improvement in architectural efficiency for throughput kernels.

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Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU

GPU architectures are increasingly important in the multi-core era due to their high number of parallel processors. Programming thousands of massively parallel threads is a big challenge for software engineers, but understanding the performance bottlenecks of those parallel programs on GPU architectures to improve application performance is even more difficult. Current approaches rely on programmers to tune their applications by exploiting the design space exhaustively without fully understanding the performance characteristics of their applications.To provide insights into the performance bottlenecks of parallel applications on GPU architectures, we propose a simple analytical model that estimates the execution time of massively parallel programs. The key component of our model is estimating the number of parallel memory requests (we call this the memory warp parallelism) by considering the number of running threads and memory bandwidth. Based on the degree of memory warp parallelism, the model estimates the cost of memory requests, thereby estimating the overall execution time of a program. Comparisons between the outcome of the model and the actual execution time in several GPUs show that the geometric mean of absolute error of our model on micro-benchmarks is 5.4% and on GPU computing applications is 13.3%. All the applications are written in the CUDA programming language.

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An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness

Chip-level power and thermal implications will continue to rule as one of the primary design constraints and performance limiters. The gap between average and peak power actually widens with increased levels of core integration. As such, if per-core control of power levels (modes) is possible, a global power manager should be able to dynamically set the modes suitably. This would be done in tune with the workload characteristics, in order to always maintain a chip-level power that is below the specified budget. Furthermore, this should be possible without significant degradation of chip-level throughput performance. We analyze and validate this concept in detail in this paper. We assume a per-core DVFS (dynamic voltage and frequency scaling) knob to be available to such a conceptual global power manager. We evaluate several different policies for global multi-core power management. In this analysis, we consider various different objectives such as prioritization and optimized throughput. Overall, our results show that in the context of a workload comprised of SPEC benchmark threads, our best architected policies can come within 1% of the performance of an ideal oracle, while meeting a given chip-level power budget. Furthermore, we show that these global dynamic management policies perform significantly better than static management, even if static scheduling is given oracular knowledge.

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An Analysis of Efficient Multi-Core Global Power Management Policies: Maximizing Performance for a Given Power Budget

This paper presents a many-core visual computing architecture code named Larrabee, a new software rendering pipeline, a manycore programming model, and performance analysis for several applications. Larrabee uses multiple in-order x86 CPU cores that are augmented by a wide vector processor unit, as well as some fixed function logic blocks. This provides dramatically higher performance per watt and per unit of area than out-of-order CPUs on highly parallel workloads. It also greatly increases the flexibility and programmability of the architecture as compared to standard GPUs. A coherent on-die 2nd level cache allows efficient inter-processor communication and high-bandwidth local data access by CPU cores. Task scheduling is performed entirely with software in Larrabee, rather than in fixed function logic. The customizable software graphics rendering pipeline for this architecture uses binning in order to reduce required memory bandwidth, minimize lock contention, and increase opportunities for parallelism relative to standard GPUs. The Larrabee native programming model supports a variety of highly parallel applications that use irregular data structures. Performance analysis on those applications demonstrates Larrabee's potential for a broad range of parallel computation.

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Larrabee: a many-core x86 architecture for visual computing

This paper is motivated by three recent trends in computer design. First, chip multi-processors (CMPs) with increasing numbers of CPU cores per chip are becoming common. Second, multi-threaded software that can take advantage of CMPs will soon become prevalent. Due to the nature of the algorithms, these multi-threaded programs inherently will have phases of sequential execution; Amdahlýs law dictates that the speedup of such parallel programs will be limited by the sequential portion of the computation. Finally, increasing levels of on-chip integration coupled with a slowing rate of reduction in supply voltage make power consumption a first order design constraint. Given this environment, our goal is to minimize the execution times of multi-threaded programs containing nontrivial parallel and sequential phases, while keeping the CMPýs total power consumption within a fixed budget. In order to mitigate the effects of Amdahlýs law, in this paper we make a compelling case for varying the amount of energy expended to process instructions according to the amount of available parallelism. Using the equation, Power=Energy per instruction (EPI) * Instructions per second (IPS), we propose that during phases of limited parallelism (low IPS) the chip multi-processor will spend more EPI; similarly, during phases of higher parallelism (high IPS) the chip multi-processor will spend less EPI; in both scenarios power is fixed. We evaluate the performance benefits of an EPI throttle on an asymmetric multiprocessor (AMP) prototyped from a physical 4-way Xeon SMP server. Using a wide range of multi-threaded programs, we show a 38% wall clock speedup on an AMP compared to a standard SMP that uses the same power. We also measure the supply current on a 4-way SMP server while running the multi-threaded programs and use the measured data as input to a software simulator that implements a more flexible EPI throttle. The results from the measurement-driven simulation show performance benefits comparable to the AMP prototype. We analyze the results from both techniques, explain why and when an EPI throttle works well, and conclude with a discussion of the challenges in building practical EPI throttles.

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Mitigating Amdahl's Law through EPI Throttling

In an embodiment, a method is provided. The method includes managing user-level threads on a first instruction sequencer in response to executing user-level instructions on a second instruction sequencer that is under control of an application level program. A first user-level thread is run on the second instruction sequencer and contains one or more user level instructions. A first user level instruction has at least 1) a field that makes reference to one or more instruction sequencers or 2) implicitly references with a pointer to code that specifically addresses one or more instruction sequencers when the code is executed.

Mechanism for instruction set based thread execution on a plurality of instruction sequencers

A first execution time of a first thread executing on a first processing unit of a multiprocessor is determined. A second execution time of a second thread executing on a second processing unit of the multiprocessor is determined, the first and second threads executing in parallel. Power is set to the first and second processing units to effectuate the first and second threads to finish executing at approximately the same time in future executions of the first and second threads. Other embodiments are also described and claimed.

Load balancing for multi-threaded applications via asymmetric power throttling

Traditionally, architectural innovations designed to boost single-threaded performance incur overhead costs which significantly increase power consumption. In many cases the increase in power exceeds the improvement in performance, resulting in a net increase in energy consumption. Thus, it is reasonable to assume that modern attempts to improve singlethreaded performance will have a negative impact on energy efficiency. This has led to the belief that "Big Cores" are inherently inefficient. To the contrary, we present a study which finds that the increased complexity of the core microarchitecture in recent generations of the IntelR Core™ processor have reduced both the time and energy required to run various workloads. Moreover, taking out the impact of process technology changes, our study still finds the architecture and microarchitecture changes ---such as the increase in SIMD width, addition of the frontend caches, and the enhancement to the out-of-order execution engine--- account for 1.2x improvement in energy efficiency for these processors. This paper provides real-world examples of how architectural innovations can mitigate inefficiencies associated with "Big Cores" ---for example, micro-op caches obviate the costly decode of complex x86 instructions--- resulting in a core architecture that is both high performance and energy efficient. It also contributes to the understanding of how microarchitecture affects performance, power and energy efficiency by modeling the relationship between them

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Ed Grochowski

Papers

Larrabee: a many-core x86 architecture for visual computing

Mitigating Amdahl's Law through EPI Throttling

Mechanism for instruction set based thread execution on a plurality of instruction sequencers

Load balancing for multi-threaded applications via asymmetric power throttling

Improving the energy efficiency of big cores