icestorm: improve x86 build/runtime perf with pypy
PyPy3 offers tremendous speedups for IceStorm tools written in Python,
including tools used at compile-time to generate the chip databases, and
runtime tools distributed to users, such as icebox_vlog.
For example, on my ThreadRipper 1950X, build times for IceStorm
consistently go from 2m30s -> 1m30s with this change, a 40% improvement,
simply due to improvements in raw CPU efficiency. (This is also worsened
by the fact the build is currently serial, but that can easily be fixed
anyway.)
On top of that, tools distributed to users are also now run using PyPy.
Utilities such as icebox_vlog are useful for post-bitstream testing, for
instance, and also are improved due to improved CPU efficiency as well.
For example, when "decompiling" an ICE40 bitstream for HX8K devices,
containing a synthesized copy of PicoRV32 (from the NextPNR demos), the
runtime of icebox_vlog is cut from 25 seconds to 9 seconds consistently
with this change alone.
Normally, picking a Python interpreter outright for Python-based code is
a "bad idea", but in the case of IceStorm it should be perfectly safe,
and an excellent improvement for users. There are a few reasons for
this:
- IceStorm uses pure Python 3 and nothing else. There are no
requirements for any 3rd party packages, which might cause annoying
incompatibilities, and PyPy has historically shown very strong core
Python compatibility.
- IceStorm is NOT a set of Python libraries, it is a set of tools,
some of which, coincidentally, are written in Python. It is (normally)
bad form to fix libraries to certain interpreters versions if the reason
strictly isn't "it doesn't work/isn't compatible". That is not the case
here. These tools may later be used by other programs, such as NextPNR,
but the Python interpreter is ultimately not that important in quesion
for the user. In this sense, there is almost no downside to picking
PyPy explicitly if it offers far better performance.
(Point 2 is not actually strictly true; there are some distributed .py
files that you can import from but they are basically just static
classes that are imported by tools like nextpnr; this is expected.)
Because of this, users should see very little change except better
performance for IceStorm tools on their machines.
Note that PyPy is not supported on aarch64 -- this only applies to
x86_64 machines.
Signed-off-by: Austin Seipp <aseipp@pobox.com>
2019-01-10 00:08:06 +00:00
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{ stdenv, fetchFromGitHub
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, pkgconfig, libftdi
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, python3, pypy3
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2019-08-14 00:38:56 +01:00
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# PyPy yields large improvements in build time and runtime performance,
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# and IceStorm isn't intended to be used as a library other than by the
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# nextpnr build process (which is also sped up by using PyPy), so we
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# use it by default. See 18839e1 for more details.
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, usePyPy ? stdenv.isx86_64 /* pypy3 seems broken on i686 */
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}:
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2015-12-29 16:29:44 +00:00
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stdenv.mkDerivation rec {
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2019-08-14 00:38:56 +01:00
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pname = "icestorm";
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version = "2019.08.08";
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pythonPkg = if usePyPy then pypy3 else python3;
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pythonInterp = pythonPkg.interpreter;
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2015-12-29 16:29:44 +00:00
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src = fetchFromGitHub {
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2017-11-05 17:27:24 +00:00
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owner = "cliffordwolf";
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repo = "icestorm";
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2019-08-14 00:38:56 +01:00
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rev = "2ccae0d3864fd7268118287a85963c0116745cff";
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sha256 = "1vlk5k7x6c1bjp19niyl0shljj8il94q2brjmda1rwhqxz81g9s7";
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2015-12-29 16:29:44 +00:00
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};
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2017-12-07 04:16:07 +00:00
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nativeBuildInputs = [ pkgconfig ];
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icestorm: improve x86 build/runtime perf with pypy
PyPy3 offers tremendous speedups for IceStorm tools written in Python,
including tools used at compile-time to generate the chip databases, and
runtime tools distributed to users, such as icebox_vlog.
For example, on my ThreadRipper 1950X, build times for IceStorm
consistently go from 2m30s -> 1m30s with this change, a 40% improvement,
simply due to improvements in raw CPU efficiency. (This is also worsened
by the fact the build is currently serial, but that can easily be fixed
anyway.)
On top of that, tools distributed to users are also now run using PyPy.
Utilities such as icebox_vlog are useful for post-bitstream testing, for
instance, and also are improved due to improved CPU efficiency as well.
For example, when "decompiling" an ICE40 bitstream for HX8K devices,
containing a synthesized copy of PicoRV32 (from the NextPNR demos), the
runtime of icebox_vlog is cut from 25 seconds to 9 seconds consistently
with this change alone.
Normally, picking a Python interpreter outright for Python-based code is
a "bad idea", but in the case of IceStorm it should be perfectly safe,
and an excellent improvement for users. There are a few reasons for
this:
- IceStorm uses pure Python 3 and nothing else. There are no
requirements for any 3rd party packages, which might cause annoying
incompatibilities, and PyPy has historically shown very strong core
Python compatibility.
- IceStorm is NOT a set of Python libraries, it is a set of tools,
some of which, coincidentally, are written in Python. It is (normally)
bad form to fix libraries to certain interpreters versions if the reason
strictly isn't "it doesn't work/isn't compatible". That is not the case
here. These tools may later be used by other programs, such as NextPNR,
but the Python interpreter is ultimately not that important in quesion
for the user. In this sense, there is almost no downside to picking
PyPy explicitly if it offers far better performance.
(Point 2 is not actually strictly true; there are some distributed .py
files that you can import from but they are basically just static
classes that are imported by tools like nextpnr; this is expected.)
Because of this, users should see very little change except better
performance for IceStorm tools on their machines.
Note that PyPy is not supported on aarch64 -- this only applies to
x86_64 machines.
Signed-off-by: Austin Seipp <aseipp@pobox.com>
2019-01-10 00:08:06 +00:00
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buildInputs = [ pythonPkg libftdi ];
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2018-02-14 06:15:06 +00:00
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makeFlags = [ "PREFIX=$(out)" ];
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2015-12-29 16:29:44 +00:00
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2019-01-10 22:08:01 +00:00
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enableParallelBuilding = true;
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2018-02-14 22:09:42 +00:00
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# fix icebox_vlog chipdb path. icestorm issue:
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# https://github.com/cliffordwolf/icestorm/issues/125
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icestorm: improve x86 build/runtime perf with pypy
PyPy3 offers tremendous speedups for IceStorm tools written in Python,
including tools used at compile-time to generate the chip databases, and
runtime tools distributed to users, such as icebox_vlog.
For example, on my ThreadRipper 1950X, build times for IceStorm
consistently go from 2m30s -> 1m30s with this change, a 40% improvement,
simply due to improvements in raw CPU efficiency. (This is also worsened
by the fact the build is currently serial, but that can easily be fixed
anyway.)
On top of that, tools distributed to users are also now run using PyPy.
Utilities such as icebox_vlog are useful for post-bitstream testing, for
instance, and also are improved due to improved CPU efficiency as well.
For example, when "decompiling" an ICE40 bitstream for HX8K devices,
containing a synthesized copy of PicoRV32 (from the NextPNR demos), the
runtime of icebox_vlog is cut from 25 seconds to 9 seconds consistently
with this change alone.
Normally, picking a Python interpreter outright for Python-based code is
a "bad idea", but in the case of IceStorm it should be perfectly safe,
and an excellent improvement for users. There are a few reasons for
this:
- IceStorm uses pure Python 3 and nothing else. There are no
requirements for any 3rd party packages, which might cause annoying
incompatibilities, and PyPy has historically shown very strong core
Python compatibility.
- IceStorm is NOT a set of Python libraries, it is a set of tools,
some of which, coincidentally, are written in Python. It is (normally)
bad form to fix libraries to certain interpreters versions if the reason
strictly isn't "it doesn't work/isn't compatible". That is not the case
here. These tools may later be used by other programs, such as NextPNR,
but the Python interpreter is ultimately not that important in quesion
for the user. In this sense, there is almost no downside to picking
PyPy explicitly if it offers far better performance.
(Point 2 is not actually strictly true; there are some distributed .py
files that you can import from but they are basically just static
classes that are imported by tools like nextpnr; this is expected.)
Because of this, users should see very little change except better
performance for IceStorm tools on their machines.
Note that PyPy is not supported on aarch64 -- this only applies to
x86_64 machines.
Signed-off-by: Austin Seipp <aseipp@pobox.com>
2019-01-10 00:08:06 +00:00
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#
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# also, fix up the path to the chosen Python interpreter. for pypy-compatible
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# platforms, it offers significant performance improvements.
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2018-02-14 22:09:42 +00:00
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patchPhase = ''
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substituteInPlace ./icebox/icebox_vlog.py \
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--replace /usr/local/share "$out/share"
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icestorm: improve x86 build/runtime perf with pypy
PyPy3 offers tremendous speedups for IceStorm tools written in Python,
including tools used at compile-time to generate the chip databases, and
runtime tools distributed to users, such as icebox_vlog.
For example, on my ThreadRipper 1950X, build times for IceStorm
consistently go from 2m30s -> 1m30s with this change, a 40% improvement,
simply due to improvements in raw CPU efficiency. (This is also worsened
by the fact the build is currently serial, but that can easily be fixed
anyway.)
On top of that, tools distributed to users are also now run using PyPy.
Utilities such as icebox_vlog are useful for post-bitstream testing, for
instance, and also are improved due to improved CPU efficiency as well.
For example, when "decompiling" an ICE40 bitstream for HX8K devices,
containing a synthesized copy of PicoRV32 (from the NextPNR demos), the
runtime of icebox_vlog is cut from 25 seconds to 9 seconds consistently
with this change alone.
Normally, picking a Python interpreter outright for Python-based code is
a "bad idea", but in the case of IceStorm it should be perfectly safe,
and an excellent improvement for users. There are a few reasons for
this:
- IceStorm uses pure Python 3 and nothing else. There are no
requirements for any 3rd party packages, which might cause annoying
incompatibilities, and PyPy has historically shown very strong core
Python compatibility.
- IceStorm is NOT a set of Python libraries, it is a set of tools,
some of which, coincidentally, are written in Python. It is (normally)
bad form to fix libraries to certain interpreters versions if the reason
strictly isn't "it doesn't work/isn't compatible". That is not the case
here. These tools may later be used by other programs, such as NextPNR,
but the Python interpreter is ultimately not that important in quesion
for the user. In this sense, there is almost no downside to picking
PyPy explicitly if it offers far better performance.
(Point 2 is not actually strictly true; there are some distributed .py
files that you can import from but they are basically just static
classes that are imported by tools like nextpnr; this is expected.)
Because of this, users should see very little change except better
performance for IceStorm tools on their machines.
Note that PyPy is not supported on aarch64 -- this only applies to
x86_64 machines.
Signed-off-by: Austin Seipp <aseipp@pobox.com>
2019-01-10 00:08:06 +00:00
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for x in icefuzz/Makefile icebox/Makefile icetime/Makefile; do
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substituteInPlace "$x" --replace python3 "${pythonInterp}"
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done
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for x in $(find . -type f -iname '*.py'); do
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substituteInPlace "$x" \
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2019-01-12 21:29:35 +00:00
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--replace '/usr/bin/env python3' '${pythonInterp}'
|
icestorm: improve x86 build/runtime perf with pypy
PyPy3 offers tremendous speedups for IceStorm tools written in Python,
including tools used at compile-time to generate the chip databases, and
runtime tools distributed to users, such as icebox_vlog.
For example, on my ThreadRipper 1950X, build times for IceStorm
consistently go from 2m30s -> 1m30s with this change, a 40% improvement,
simply due to improvements in raw CPU efficiency. (This is also worsened
by the fact the build is currently serial, but that can easily be fixed
anyway.)
On top of that, tools distributed to users are also now run using PyPy.
Utilities such as icebox_vlog are useful for post-bitstream testing, for
instance, and also are improved due to improved CPU efficiency as well.
For example, when "decompiling" an ICE40 bitstream for HX8K devices,
containing a synthesized copy of PicoRV32 (from the NextPNR demos), the
runtime of icebox_vlog is cut from 25 seconds to 9 seconds consistently
with this change alone.
Normally, picking a Python interpreter outright for Python-based code is
a "bad idea", but in the case of IceStorm it should be perfectly safe,
and an excellent improvement for users. There are a few reasons for
this:
- IceStorm uses pure Python 3 and nothing else. There are no
requirements for any 3rd party packages, which might cause annoying
incompatibilities, and PyPy has historically shown very strong core
Python compatibility.
- IceStorm is NOT a set of Python libraries, it is a set of tools,
some of which, coincidentally, are written in Python. It is (normally)
bad form to fix libraries to certain interpreters versions if the reason
strictly isn't "it doesn't work/isn't compatible". That is not the case
here. These tools may later be used by other programs, such as NextPNR,
but the Python interpreter is ultimately not that important in quesion
for the user. In this sense, there is almost no downside to picking
PyPy explicitly if it offers far better performance.
(Point 2 is not actually strictly true; there are some distributed .py
files that you can import from but they are basically just static
classes that are imported by tools like nextpnr; this is expected.)
Because of this, users should see very little change except better
performance for IceStorm tools on their machines.
Note that PyPy is not supported on aarch64 -- this only applies to
x86_64 machines.
Signed-off-by: Austin Seipp <aseipp@pobox.com>
2019-01-10 00:08:06 +00:00
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done
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2018-02-14 22:09:42 +00:00
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'';
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2015-12-29 16:29:44 +00:00
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meta = {
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description = "Documentation and tools for Lattice iCE40 FPGAs";
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longDescription = ''
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Project IceStorm aims at reverse engineering and
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documenting the bitstream format of Lattice iCE40
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FPGAs and providing simple tools for analyzing and
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creating bitstream files.
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'';
|
icestorm: improve x86 build/runtime perf with pypy
PyPy3 offers tremendous speedups for IceStorm tools written in Python,
including tools used at compile-time to generate the chip databases, and
runtime tools distributed to users, such as icebox_vlog.
For example, on my ThreadRipper 1950X, build times for IceStorm
consistently go from 2m30s -> 1m30s with this change, a 40% improvement,
simply due to improvements in raw CPU efficiency. (This is also worsened
by the fact the build is currently serial, but that can easily be fixed
anyway.)
On top of that, tools distributed to users are also now run using PyPy.
Utilities such as icebox_vlog are useful for post-bitstream testing, for
instance, and also are improved due to improved CPU efficiency as well.
For example, when "decompiling" an ICE40 bitstream for HX8K devices,
containing a synthesized copy of PicoRV32 (from the NextPNR demos), the
runtime of icebox_vlog is cut from 25 seconds to 9 seconds consistently
with this change alone.
Normally, picking a Python interpreter outright for Python-based code is
a "bad idea", but in the case of IceStorm it should be perfectly safe,
and an excellent improvement for users. There are a few reasons for
this:
- IceStorm uses pure Python 3 and nothing else. There are no
requirements for any 3rd party packages, which might cause annoying
incompatibilities, and PyPy has historically shown very strong core
Python compatibility.
- IceStorm is NOT a set of Python libraries, it is a set of tools,
some of which, coincidentally, are written in Python. It is (normally)
bad form to fix libraries to certain interpreters versions if the reason
strictly isn't "it doesn't work/isn't compatible". That is not the case
here. These tools may later be used by other programs, such as NextPNR,
but the Python interpreter is ultimately not that important in quesion
for the user. In this sense, there is almost no downside to picking
PyPy explicitly if it offers far better performance.
(Point 2 is not actually strictly true; there are some distributed .py
files that you can import from but they are basically just static
classes that are imported by tools like nextpnr; this is expected.)
Because of this, users should see very little change except better
performance for IceStorm tools on their machines.
Note that PyPy is not supported on aarch64 -- this only applies to
x86_64 machines.
Signed-off-by: Austin Seipp <aseipp@pobox.com>
2019-01-10 00:08:06 +00:00
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homepage = http://www.clifford.at/icestorm/;
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license = stdenv.lib.licenses.isc;
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2017-10-16 08:24:02 +01:00
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maintainers = with stdenv.lib.maintainers; [ shell thoughtpolice ];
|
icestorm: improve x86 build/runtime perf with pypy
PyPy3 offers tremendous speedups for IceStorm tools written in Python,
including tools used at compile-time to generate the chip databases, and
runtime tools distributed to users, such as icebox_vlog.
For example, on my ThreadRipper 1950X, build times for IceStorm
consistently go from 2m30s -> 1m30s with this change, a 40% improvement,
simply due to improvements in raw CPU efficiency. (This is also worsened
by the fact the build is currently serial, but that can easily be fixed
anyway.)
On top of that, tools distributed to users are also now run using PyPy.
Utilities such as icebox_vlog are useful for post-bitstream testing, for
instance, and also are improved due to improved CPU efficiency as well.
For example, when "decompiling" an ICE40 bitstream for HX8K devices,
containing a synthesized copy of PicoRV32 (from the NextPNR demos), the
runtime of icebox_vlog is cut from 25 seconds to 9 seconds consistently
with this change alone.
Normally, picking a Python interpreter outright for Python-based code is
a "bad idea", but in the case of IceStorm it should be perfectly safe,
and an excellent improvement for users. There are a few reasons for
this:
- IceStorm uses pure Python 3 and nothing else. There are no
requirements for any 3rd party packages, which might cause annoying
incompatibilities, and PyPy has historically shown very strong core
Python compatibility.
- IceStorm is NOT a set of Python libraries, it is a set of tools,
some of which, coincidentally, are written in Python. It is (normally)
bad form to fix libraries to certain interpreters versions if the reason
strictly isn't "it doesn't work/isn't compatible". That is not the case
here. These tools may later be used by other programs, such as NextPNR,
but the Python interpreter is ultimately not that important in quesion
for the user. In this sense, there is almost no downside to picking
PyPy explicitly if it offers far better performance.
(Point 2 is not actually strictly true; there are some distributed .py
files that you can import from but they are basically just static
classes that are imported by tools like nextpnr; this is expected.)
Because of this, users should see very little change except better
performance for IceStorm tools on their machines.
Note that PyPy is not supported on aarch64 -- this only applies to
x86_64 machines.
Signed-off-by: Austin Seipp <aseipp@pobox.com>
2019-01-10 00:08:06 +00:00
|
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platforms = stdenv.lib.platforms.linux;
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2015-12-29 16:29:44 +00:00
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};
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}
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