Impact’ via Morgan Stanley (10/23) Enabling Infrastructure CPUs Big Data / Cloud GPUs Computing Cycles Over Time – 1960s-2020s, per Morgan Stanley Note: Axis is logarithmic; i.e., there are expected mechanical, and software systems, new data centers are going up at speeds that resemble consumer tech cycles more than real estate development. But beneath that velocity lies a capital model that’s anything losers. Alasdair Nairn’s ‘Engines That Move Markets’ (link here) distills two centuries of such cycles, and his observations are prescient for today’s AI boom. Highlights of his observations follow…

Arguments passed to self.stop are returned from self.wait. It is possible to have multiple wait/stop cycles in the same test. Example: # This test uses coroutine style. class MyTestCase(AsyncTestCase): program on any failure but stops after the first successful run. tornado.concurrent • Fixed reference cycles that could lead to increased memory usage. tornado.escape • Several methods in this module now been updated to match the updated signature of asyncio.loop.run_in_executor. • Fixed reference cycles that could lead to increased memory usage. 6.9. Release notes 169Tornado Documentation, Release

Arguments passed to self.stop are returned from self.wait. It is possible to have multiple wait/stop cycles in the same test. Example: # This test uses coroutine style. class MyTestCase(AsyncTestCase): program on any failure but stops after the first successful run. tornado.concurrent Fixed reference cycles that could lead to increased memory usage. tornado.escapeSeveral methods in this module now simply [https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.loop.run_in_executor]. Fixed reference cycles that could lead to increased memory usage. tornado.locale format_timestamp now supports “aware”

allows garbage collection to run. Note that even though safepoints are fast (typically around 2 clock cycles), they can still degrade performance if called in a tight loop. Julia 1.4 This function is available Iterators.cycle – Function. cycle(iter[, n::Int]) An iterator that cycles through iter forever. If n is specified, then it cycles through iter that many times. When iter is empty, so are cycle(iter) estimate can be computed by walking all of the edges contents and summing those, while accounting for cycles and duplicates. (This field currently does not include any measured AOT compile times.) CodeInfo

allows garbage collection to run. Note that even though safepoints are fast (typically around 2 clock cycles), they can still degrade performance if called in a tight loop. Julia 1.4 This function is available Iterators.cycle – Function. cycle(iter[, n::Int]) An iterator that cycles through iter forever. If n is specified, then it cycles through iter that many times. When iter is empty, so are cycle(iter) estimate can be computed by walking all of the edges contents and summing those, while accounting for cycles and duplicates. (This field currently does not include any measured AOT compile times.) CodeInfo

allows garbage collection to run. Note that even though safepoints are fast (typically around 2 clock cycles), they can still degrade performance if called in a tight loop. Julia 1.4 This function is available Iterators.cycle – Function. cycle(iter[, n::Int]) An iterator that cycles through iter forever. If n is specified, then it cycles through iter that many times. When iter is empty, so are cycle(iter) estimate can be computed by walking all of the edges contents and summing those, while accounting for cycles and duplicates. (This field currently does not include any measured AOT compile times.) CodeInfo

allows garbage collection to run. Note that even though safepoints are fast (typically around 2 clock cycles), they can still degrade performance if called in a tight loop. Julia 1.4 This function is available Iterators.cycle – Function. cycle(iter[, n::Int]) An iterator that cycles through iter forever. If n is specified, then it cycles through iter that many times. When iter is empty, so are cycle(iter) estimate can be computed by walking all of the edges contents and summing those, while accounting for cycles and duplicates. (This field currently does not include any measured AOT compile times.) CodeInfo

allows garbage collection to run. Note that even though safepoints are fast (typically around 2 clock cycles), they can still degrade performance if called in a tight loop. Julia 1.4 This function is available Iterators.cycle – Function. cycle(iter[, n::Int]) An iterator that cycles through iter forever. If n is specified, then it cycles through iter that many times. When iter is empty, so are cycle(iter) estimate can be computed by walking all of the edges contents and summing those, while accounting for cycles and duplicates. (This field currently does not include any measured AOT compile times.) CodeInfo

Iterators.cycle – Function. cycle(iter[, n::Int]) An iterator that cycles through iter forever. If n is specified, then it cycles through iter that many times. When iter is empty, so are cycle(iter) table, implemented in add_tfunc and its callers, that assigns an estimated cost (measured in CPU cycles) to each of Julia's intrinsic functions. These costs are based on standard ranges for common architectures which all input and output types were inferred in advance) is assigned a fixed cost (currently 20 cycles). In contrast, a :call expression, for functions other than intrinsics/builtins, indicates that the

Iterators.cycle – Function. cycle(iter[, n::Int]) An iterator that cycles through iter forever. If n is specified, then it cycles through iter that many times. When iter is empty, so are cycle(iter) table, implemented in add_tfunc and its callers, that assigns an estimated cost (measured in CPU cycles) to each of Julia's intrinsic functions. These costs are based on standard ranges for common architectures which all input and output types were inferred in advance) is assigned a fixed cost (currently 20 cycles). In contrast, a :call expression, for functions other than intrinsics/builtins, indicates that the