6695e5ef70
This change adds source code for SpatterGen ClockedObject. The set of source code pushed includes code for SpatterKernel that tracks whether information is being gathered or scattered as well as the list of indices to be accessed. This model has PyBindMethod to add SpatterKernels from python. This way all the preparations for kernels can be done in python. SpatterGen has a few parameters that model limits on a few of hardware resources in the backend of a processor, e.g. number of functional units to calculate effective address, the latency of calculating effective address, number of integer registers. Change-Id: I451ffb385180a914e884cab220928c5f1944b2e3
116 lines
5.1 KiB
Python
116 lines
5.1 KiB
Python
# Copyright (c) 2024 The Regents of The University of California
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are
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# met: redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer;
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# redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution;
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# neither the name of the copyright holders nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
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# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
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# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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from m5.citations import add_citation
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from m5.objects.ClockedObject import ClockedObject
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from m5.params import *
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from m5.proxy import *
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from m5.util.pybind import PyBindMethod
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class SpatterKernelType(Enum):
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vals = ["scatter", "gather"]
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class SpatterProcessingMode(Enum):
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vals = ["synchronous", "asynchronous"]
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class SpatterGen(ClockedObject):
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type = "SpatterGen"
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cxx_header = "cpu/testers/spatter_gen/spatter_gen.hh"
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cxx_class = "gem5::SpatterGen"
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system = Param.System(Parent.any, "System this SpatterGen is a part of.")
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processing_mode = Param.SpatterProcessingMode(
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"How to process kernels accross multiple SpatterGen cores. "
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"Whether to synchronize on kernel boundaries or not."
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)
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port = RequestPort("Port to send memory requests.")
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int_regfile_size = Param.Int("Size of the integer register file.")
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fp_regfile_size = Param.Int("Size of the floating point register file.")
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request_gen_latency = Param.Cycles(
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"Number of cycles to spend for creating a request."
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)
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request_gen_rate = Param.Int("Number of requests generate per cycle.")
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request_buffer_entries = Param.Int("Size of the request buffer.")
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send_rate = Param.Int(
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"Number of requests to send in parallel."
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"Emulates the number of dcache ports."
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)
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cxx_exports = [
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PyBindMethod("addKernel"),
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PyBindMethod("proceedPastSyncPoint"),
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]
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add_citation(
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SpatterGen,
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"""@inproceedings{10.1145/3422575.3422794,
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author = {Lavin, Patrick and Young, Jeffrey and Vuduc, Richard and Riedy,
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Jason and Vose, Aaron and Ernst, Daniel},
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title = {Evaluating Gather and Scatter Performance on CPUs and GPUs},
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year = {2021},
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isbn = {9781450388993},
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publisher = {Association for Computing Machinery},
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address = {New York, NY, USA},
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url = {https://doi.org/10.1145/3422575.3422794},
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doi = {10.1145/3422575.3422794},
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abstract = {This paper describes a new benchmark tool,
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Spatter, for assessing memory system architectures in the context of a
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specific category of indexed accesses known as gather and scatter.
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These types of operations are increasingly used to express sparse and
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irregular data access patterns, and they have widespread utility in many
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modern HPC applications including scientific simulations, data mining and
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analysis computations, and graph processing. However, many traditional
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benchmarking tools like STREAM, STRIDE, and GUPS focus on characterizing
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only uniform stride or fully random accesses despite evidence that modern
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applications use varied sets of more complex access patterns. Spatter is an
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open-source benchmark that provides a tunable and configurable framework to
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benchmark a variety of indexed access patterns, including variations of gather
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/ scatter that are seen in HPC mini-apps evaluated in this work. The design of
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Spatter includes backends for OpenMP and CUDA, and experiments show how it can
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be used to evaluate 1) uniform access patterns for CPU and GPU, 2) prefetching
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regimes for gather / scatter, 3) compiler implementations of vectorization for
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gather / scatter, and 4) trace-driven “proxy patterns” that reflect the
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patterns found in multiple applications. The results from Spatter experiments
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show, for instance, that GPUs typically outperform CPUs for these operations
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in absolute bandwidth but not fraction of peak bandwidth, and that Spatter can
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better represent the performance of some cache-dependent mini-apps than
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traditional STREAM bandwidth measurements.},
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booktitle = {Proceedings of the International Symposium on Memory Systems},
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pages = {209–222},
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numpages = {14},
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location = {Washington, DC, USA},
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series = {MEMSYS '20}
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}
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""",
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)
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