122 research outputs found
Communion: a new strategy form memory management in high-performance computer
Modern computers present a big gap between peak performance and sustained performance. There are many reasons for this situation, but mainly involving an inefficient usage of computational resources. Nowadays the memory system is the most critical component because of its growing inability to keep up with the processor requests. Technological trends have produced a large and growing gap between CPU speeds and DRAM speeds. Much research has focused this memory system problem, including program optimizing techniques, data locality enhancement, hardware and software prefetching, decoupled architectures, multithreading, speculative loads and execution. These techniques have got a relative success, but they focus only one component in the hardware or software systems. We present here a new strategy for memory management in high-performance computer systems, named COMMUNION. The basic idea behind this strategy is "cooperation". We introduce some interaction possibilities among system programs that are responsible to generate and execute application programs. So, we investigate two specific interactions: between the compiler and the operating system, and among the compiling system components. The experimental results show that it's possible to get improvements of about 10 times in execution time, and about 5 times in memory demand, enhancing the interaction between the compiling system components. In the interaction between compiler and operating system, named Compiler-Aided Page Replacement (CAPR), we achieved a reduction of about 10% in space-time product, with an increase of only 0.5% in the total execution time. All these results show that it s possible to manage main memory with a better efficiency than current systems.Facultad de Informátic
Communion: a new strategy for memory management in high-performance computer systems
Modern computers present a big gap between peak performance and sustained performance. There are many reasons for this situation, but mainly involving an inefficient usage of computational resources. Nowadays the memory system is the most critical component because of its growing inability to keep up with the processor requests. Technological trends have produced a large and growing gap between CPU speeds and DRAM speeds.
Much research has focused this memory system problem, including program optimizing techniques, data locality enhancement, hardware and software prefetching, decoupled architectures, mutithreading, speculative loads and execution. These techniques have got a relative success, but they focus only one component in the hardware or software systems.
We present here a new strategy for memory management in high-performance computer systems, named COMMUNION. The basic idea behind this strategy is cooperation. We introduce some interaction possibilities among system programs that are responsible to generate and execute application programs. So, we investigate two specific interactions: between the compiler and the operating system, and among the compiling system components.
The experimental results show that it’s possible to get improvements of about 10 times in execution time, and about 5 times in memory demand. In the interaction between compiler and operating system, named Compiler-Aided Page Replacement (CAPR), we achieved a reduction of about 10% in space-time product, with an increase of only 0.5% in the total execution time. All these results show that it’s possible to manage main memory with a better efficiency than current systems.Eje: Procesamiento distribuido y paralelo. Tratamiento de señalesRed de Universidades con Carreras en Informática (RedUNCI
Communion: a new strategy for memory management in high-performance computer systems
Modern computers present a big gap between peak performance and sustained performance. There are many reasons for this situation, but mainly involving an inefficient usage of computational resources. Nowadays the memory system is the most critical component because of its growing inability to keep up with the processor requests. Technological trends have produced a large and growing gap between CPU speeds and DRAM speeds.
Much research has focused this memory system problem, including program optimizing techniques, data locality enhancement, hardware and software prefetching, decoupled architectures, mutithreading, speculative loads and execution. These techniques have got a relative success, but they focus only one component in the hardware or software systems.
We present here a new strategy for memory management in high-performance computer systems, named COMMUNION. The basic idea behind this strategy is cooperation. We introduce some interaction possibilities among system programs that are responsible to generate and execute application programs. So, we investigate two specific interactions: between the compiler and the operating system, and among the compiling system components.
The experimental results show that it’s possible to get improvements of about 10 times in execution time, and about 5 times in memory demand. In the interaction between compiler and operating system, named Compiler-Aided Page Replacement (CAPR), we achieved a reduction of about 10% in space-time product, with an increase of only 0.5% in the total execution time. All these results show that it’s possible to manage main memory with a better efficiency than current systems.Eje: Procesamiento distribuido y paralelo. Tratamiento de señalesRed de Universidades con Carreras en Informática (RedUNCI
A Survey on Compiler Autotuning using Machine Learning
Since the mid-1990s, researchers have been trying to use machine-learning
based approaches to solve a number of different compiler optimization problems.
These techniques primarily enhance the quality of the obtained results and,
more importantly, make it feasible to tackle two main compiler optimization
problems: optimization selection (choosing which optimizations to apply) and
phase-ordering (choosing the order of applying optimizations). The compiler
optimization space continues to grow due to the advancement of applications,
increasing number of compiler optimizations, and new target architectures.
Generic optimization passes in compilers cannot fully leverage newly introduced
optimizations and, therefore, cannot keep up with the pace of increasing
options. This survey summarizes and classifies the recent advances in using
machine learning for the compiler optimization field, particularly on the two
major problems of (1) selecting the best optimizations and (2) the
phase-ordering of optimizations. The survey highlights the approaches taken so
far, the obtained results, the fine-grain classification among different
approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our
Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated
quarterly here (Send me your new published papers to be added in the
subsequent version) History: Received November 2016; Revised August 2017;
Revised February 2018; Accepted March 2018
Recommended from our members
A SIMD architecture for hard real-time systems
Emerging safety-critical systems require high-performance data-parallel architectures and, problematically, ones that can guarantee tight and safe worst-case execution times. Given the complexity of existing architectures like GPUs, it is unlikely that sufficiently accurate models and algorithms for timing analysis will emerge in the foreseeable future. This motivates a clean-slate approach to designing a real-time data-parallel architecture.
In this work I present Sim-D: a wide-SIMD architecture for hard real-time systems. Similar to GPUs, Sim-D performs hardware strip-mining to schedule the work for a compute kernel in entities called work-groups. Sim-D schedules the work for each work-group as a sequence of uninterruptible access- and execute program phases, interleaving the phases of two work-groups. By providing performance isolation between the memory- and compute resources, the execution time of each phase can be tightly bound through static analysis.
I present a predictable closed-page DRAM controller that processes requests for large 1D- and 2D blocks of data, as well as indirect indexed transfers. These large transfers coalesce the data requests of a whole work-group. For a linear 4KiB transfer over a 64-bit data bus, the utilisation provably exceeds 78% for DDR4-3200AA DRAM. For 2D blocks, a well-chosen tiling configuration can achieve near-similar efficiency. I show that bounds on the execution time of indexed transfers are pessimistic by nature, but propose a novel snoopy indexed transfer mechanism that permits more reasonable bounds when the buffer size is limited.
Finally, I present a worst-case execution time calculation algorithm for Sim-D. This algorithm is paired with two hardware work-group scheduling policies that deterministically reduce run-time variance. The worst-case execution time analysis algorithm combines static control flow analysis with a simulation-based cost model for execution and DRAM transfers. Its key novelty is the addition of a stage that considers work-group scheduling effects. I show that the work-group scheduling policies degrade performance on average by 8.9%, but permit the calculation of worst-case execution time bounds that are tight within 14.3% on average for benchmarks that avoid inefficient indexed transfers
Neurostream: Scalable and Energy Efficient Deep Learning with Smart Memory Cubes
open4siHigh-performance computing systems are moving towards 2.5D and 3D memory hierarchies, based on High Bandwidth Memory (HBM) and Hybrid Memory Cube (HMC) to mitigate the main memory bottlenecks. This trend is also creating new opportunities to revisit near-memory computation. In this paper, we propose a flexible processor-in-memory (PIM) solution for scalable and energy-efficient execution of deep convolutional networks (ConvNets), one of the fastest-growing workloads for servers and high-end embedded systems. Our co-design approach consists of a network of Smart Memory Cubes (modular extensions to the standard HMC) each augmented with a many-core PIM platform called NeuroCluster. NeuroClusters have a modular design based on NeuroStream coprocessors (for Convolution-intensive computations) and general-purpose RISC-V cores. In addition, a DRAM-friendly tiling mechanism and a scalable computation paradigm are presented to efficiently harness this computational capability with a very low programming effort. NeuroCluster occupies only 8 percent of the total logic-base (LoB) die area in a standard HMC and achieves an average performance of 240 GFLOPS for complete execution of full-featured state-of-the-art (SoA) ConvNets within a power budget of 2.5 W. Overall 11 W is consumed in a single SMC device, with 22.5 GFLOPS/W energy-efficiency which is 3.5X better than the best GPU implementations in similar technologies. The minor increase in system-level power and the negligible area increase make our PIM system a cost-effective and energy efficient solution, easily scalable to 955 GFLOPS with a small network of just four SMCs.openAzarkhish, Erfan*; Rossi, Davide; Loi, Igor; Benini, LucaAzarkhish, Erfan*; Rossi, Davide; Loi, Igor; Benini, Luc
A metadata-enhanced framework for high performance visual effects
This thesis is devoted to reducing the interactive latency of image processing computations in
visual effects. Film and television graphic artists depend upon low-latency feedback to receive
a visual response to changes in effect parameters. We tackle latency with a domain-specific optimising
compiler which leverages high-level program metadata to guide key computational and
memory hierarchy optimisations. This metadata encodes static and dynamic information about
data dependence and patterns of memory access in the algorithms constituting a visual effect –
features that are typically difficult to extract through program analysis – and presents it to the
compiler in an explicit form. By using domain-specific information as a substitute for program
analysis, our compiler is able to target a set of complex source-level optimisations that a vendor
compiler does not attempt, before passing the optimised source to the vendor compiler for
lower-level optimisation.
Three key metadata-supported optimisations are presented. The first is an adaptation of
space and schedule optimisation – based upon well-known compositions of the loop fusion and
array contraction transformations – to the dynamic working sets and schedules of a runtimeparameterised
visual effect. This adaptation sidesteps the costly solution of runtime code generation
by specialising static parameters in an offline process and exploiting dynamic metadata to
adapt the schedule and contracted working sets at runtime to user-tunable parameters. The second
optimisation comprises a set of transformations to generate SIMD ISA-augmented source code.
Our approach differs from autovectorisation by using static metadata to identify parallelism, in
place of data dependence analysis, and runtime metadata to tune the data layout to user-tunable
parameters for optimal aligned memory access. The third optimisation comprises a related set
of transformations to generate code for SIMT architectures, such as GPUs. Static dependence
metadata is exploited to guide large-scale parallelisation for tens of thousands of in-flight threads.
Optimal use of the alignment-sensitive, explicitly managed memory hierarchy is achieved by identifying
inter-thread and intra-core data sharing opportunities in memory access metadata.
A detailed performance analysis of these optimisations is presented for two industrially developed
visual effects. In our evaluation we demonstrate up to 8.1x speed-ups on Intel and AMD
multicore CPUs and up to 6.6x speed-ups on NVIDIA GPUs over our best hand-written implementations
of these two effects. Programmability is enhanced by automating the generation of
SIMD and SIMT implementations from a single programmer-managed scalar representation
EVALUATING THE IMPACT OF MEMORY SYSTEM PERFORMANCE ON SOFTWARE PREFETCHING AND LOCALITY OPTIMIZATIONS
Software prefetching and locality optimizations are two techniques for
overcoming the speed gap between processor and memory known as the
memory wall as suggested by Wulf and Mckee. This
thesis evaluates the impact of memory trends on the effectiveness
of software prefetching and locality optimizations for three types
of applications: regular scientific codes, irregular scientific
codes, and pointer-chasing codes. For many applications, software
prefetching outperforms locality optimizations when there is
sufficient bandwidth in the underlying memory system, but locality
optimizations outperform software prefetching when the underlying
memory system doesn't provide sufficient bandwidth. The break-even
point, or equivalently the crossover bandwidth point, occurs at
roughly 2.4 GBytes/sec , for 1 GHz processors on today's memory systems, and will increase on future memory systems. This thesis
also studies the interactions between software prefetching and
locality optimizations when applied in concert. Naively combining
the two techniques provides a more robust application performance
in the face of variations in memory bandwidth and/or latency, but
does not yield additional performance gains. In other words, the
performance won't be better than the best performance of the two
techniques alone. Also, several algorithms are proposed and
evaluated to better combine software prefetching and locality
optimizations, including an enhanced tiling algorithm, padding for software prefetching, and index prefetching.
(Also UMIACS-TR-2002-72
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