5,877 research outputs found
Preemptive Thread Block Scheduling with Online Structural Runtime Prediction for Concurrent GPGPU Kernels
Recent NVIDIA Graphics Processing Units (GPUs) can execute multiple kernels
concurrently. On these GPUs, the thread block scheduler (TBS) uses the FIFO
policy to schedule their thread blocks. We show that FIFO leaves performance to
chance, resulting in significant loss of performance and fairness. To improve
performance and fairness, we propose use of the preemptive Shortest Remaining
Time First (SRTF) policy instead. Although SRTF requires an estimate of runtime
of GPU kernels, we show that such an estimate of the runtime can be easily
obtained using online profiling and exploiting a simple observation on GPU
kernels' grid structure. Specifically, we propose a novel Structural Runtime
Predictor. Using a simple Staircase model of GPU kernel execution, we show that
the runtime of a kernel can be predicted by profiling only the first few thread
blocks. We evaluate an online predictor based on this model on benchmarks from
ERCBench, and find that it can estimate the actual runtime reasonably well
after the execution of only a single thread block. Next, we design a thread
block scheduler that is both concurrent kernel-aware and uses this predictor.
We implement the SRTF policy and evaluate it on two-program workloads from
ERCBench. SRTF improves STP by 1.18x and ANTT by 2.25x over FIFO. When compared
to MPMax, a state-of-the-art resource allocation policy for concurrent kernels,
SRTF improves STP by 1.16x and ANTT by 1.3x. To improve fairness, we also
propose SRTF/Adaptive which controls resource usage of concurrently executing
kernels to maximize fairness. SRTF/Adaptive improves STP by 1.12x, ANTT by
2.23x and Fairness by 2.95x compared to FIFO. Overall, our implementation of
SRTF achieves system throughput to within 12.64% of Shortest Job First (SJF, an
oracle optimal scheduling policy), bridging 49% of the gap between FIFO and
SJF.Comment: 14 pages, full pre-review version of PACT 2014 poste
Analysis, classification and comparison of scheduling techniques for software transactional memories
Transactional Memory (TM) is a practical programming paradigm for developing concurrent applications. Performance is a critical factor for TM implementations, and various studies demonstrated that specialised transaction/thread scheduling support is essential for implementing performance-effective TM systems. After one decade of research, this article reviews the wide variety of scheduling techniques proposed for Software Transactional Memories. Based on peculiarities and differences of the adopted scheduling strategies, we propose a classification of the existing techniques, and we discuss the specific characteristics of each technique. Also, we analyse the results of previous evaluation and comparison studies, and we present the results of a new experimental study encompassing techniques based on different scheduling strategies. Finally, we identify potential strengths and weaknesses of the different techniques, as well as the issues that require to be further investigated
Synapse: Synthetic Application Profiler and Emulator
We introduce Synapse motivated by the needs to estimate and emulate workload
execution characteristics on high-performance and distributed heterogeneous
resources. Synapse has a platform independent application profiler, and the
ability to emulate profiled workloads on a variety of heterogeneous resources.
Synapse is used as a proxy application (or "representative application") for
real workloads, with the added advantage that it can be tuned at arbitrary
levels of granularity in ways that are simply not possible using real
applications. Experiments show that automated profiling using Synapse
represents application characteristics with high fidelity. Emulation using
Synapse can reproduce the application behavior in the original runtime
environment, as well as reproducing properties when used in a different
run-time environments
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