I'm not sure if anyone is still reading this thread, but I wrote up some thoughts on the differences between Sequoia and Legion and some of the lessons we learned. Maybe this is a little too much "inside baseball", but hopefully some will enjoy it.

Sequoia was basically an answer to the question, "what could a compiler do for
you if it knew everything about your program?" And I do mean everything:
in addition to your source code Sequoia had to be given, at compile time, the
sizes of every input array in addition to the exact layout of the machine it
was to execute on. While these restrictions were lifted to some degree later
on, Sequoia basically had to be able to statically compute every task that it
would execute along with the exact sizes of all inputs and outputs. And with
this information it was able to do some pretty [fantastic
optimizations](http://theory.stanford.edu/~aiken/publications/papers/ppopp0...). I
believe, though I can't find the reference at the moment, that the compiler
was able to generate (from completely hardware agnostic source code)
implementations of BLAS routines competitive with Intel MKL. This was a pretty
mind-blowing achievement and to this day I'm not aware of any work that has
really been able to equal it.

Requiring programs to be fully static also had a dramatic cost, one which it
turned out not to be so easy to lift. In what I believe was the [final paper
to be published on
Sequoia](http://theory.stanford.edu/~aiken/publications/papers/ppopp1...),
Mike Bauer and others showed how to extend Sequoia to support limited forms of
dynamic behavior. But the solution wasn't all that satisfying and the work to
get there was fairly excruciating. Instead of continuing to work with the
language, Mike ended up switching gears to start a new project entirely,
Legion.

As an aside, one of the other lessons learned from the Sequoia project is that
research projects tend to be tied to the Ph.D. students who work on them. In
the Sequoia project, there was a gap in continuity between the first and
second generations of students to work on the project. This meant that
second-generation students like Mike basically ended up picking up a big
legacy code base on their own. This turned out to be a huge drain on
productivity and was among the reasons that Sequoia was eventually abandoned.

The biggest decision made about Legion up front was that it had to be
dynamic. This was a huge risk because the analysis performed by Sequoia
simply could not have been performed at runtime with reasonable cost. Sequoia
relied on knowing every single task that would ever execute and the exact
bounds of every array input and output. This meant that Sequoia could do
perfect alias analysis on these input and output arrays. Because Legion would
be a dynamic system, any analysis would have to be performed during the
execution of the program. There was no way we were going to do this in Legion
without fundamentally changing the abstractions of the programming model, and
frankly it wasn't obvious up front if this would even be possible to make
tractable.

One of the central insights of Legion was to [explicitly reify the notion of
data partitioning](http://legion.stanford.edu/pdfs/oopsla2013.pdf) in the
programming model. Sequoia didn't need this because it simply knew the inputs
and outputs to every task. Legion couldn't afford to pay the $O(N
\operatorname{log} N)$ cost to perform this analysis at runtime. By lifting
partitioning into a first-class primitive, we were able to radically reduce
the runtime cost of this analysis by leveraging what the user already know
about their data partitioning. Originally this required users to declare
certain properties of their partitions, such as disjointness, in order to make
things efficient. Eventually we discovered a [partitioning
sublanguage](http://legion.stanford.edu/pdfs/dpl2016.pdf) which allowed us to
statically discover most of these important properties, and to provide many
static guarantees even in the presence of data-dependent behavior.

The other big insight of Legion was what we call index tasks. Index tasks
are basically collections of tasks that can be described in $O(1)$ space. This
turns out to be essential if you want to scale your system to very large
distributed machines. Any representation which is $O(N)$ inevitably becomes a
scalability bottleneck, either because of memory-capacity constraints or
because of runtime complexity. By making the index task representation $O(1)$
we were able to design an [optimization to keep the runtime analysis
complexity $O(1)$ as well](http://legion.stanford.edu/pdfs/cr2017.pdf).

One consequence of being a dynamic runtime system is that Legion itself isn't
able to optimize the low-level code executed by the system. It seems to be a
general principle that the closer you are to the hardware, the more static you
need to be. Conceptually, Legion solves this problem by splitting it into two
parts: the kernels, or low-level code that has to execute as efficiently as
possible on the processor, and the orchestration of the parallelism that
exists between those kernels. Legion focuses nearly entirely on the upper
layer, leaving the lower layer to be handled by programmers. We've recovered
some of this capability via the [Regent programming
language](http://regent-lang.org/) which sits on top of Legion, but we haven't
taken this nearly as far as Sequoia did.

Despite being a dynamic runtime system, we still put a lot of effort into
making sure that it had a principled foundation. Legion is one of the few
runtime systems I'm aware of that has its own [type system and operational
semantics](http://legion.stanford.edu/pdfs/oopsla2013.pdf). This also made it
relatively easy to develop Regent.

With Regent in some ways we've now come full circle, since we're back to
having a programming language again. However, one key difference between
Regent and Sequoia is what happens when the compilers run into behavior they
can't analyze. In Sequoia, it's of the utmost importance that the compiler
understand your program, because if the compiler can't prove that parallelism
is safe, then it will serialize that part of the program. In contrast, if
Regent can't determine that parallelism is safe, it will simply fall back to
the runtime system. This means that in general Regent suffers from fewer
performance cliffs, or places where performance can suddenly (and sometimes
inexplicably) degrade.

There were some lessons we failed to learn from Sequoia. Sequoia made big use
of [graph-based
IRs](http://theory.stanford.edu/~aiken/publications/papers/ppopp0...) for
its compiler. This was a design I copied when developing the [RDIR
format](https://github.com/StanfordLegion/rdir) for Regent, a decision I now
consider to be a mistake. Graph-based IRs are superficially appealing because
they seem to represent the fundamental invariants that you want to reason
about. That is, if two tasks are mutually unreachable in the IR, then they are
provably safe to execute in parallel. However, working with the graph-based IR
as a representation of the program turns out to be a huge pain. RDIR has been
by far the biggest source of bugs in Regent and continues to be largest
maintenance burden. The other big motivation for a graph-based IR, that it
makes certain aliasing properties of the program more explicit than a
traditional CFG, is something that I now think could be done adequately well
in more traditional formats.

One final note about hardware support: Sequoia made a big bet on the
[Roadrunner
supercomputer](https://en.wikipedia.org/wiki/IBM_Roadrunner). Roadrunner was
an excellent match for Sequoia's programming model, because it used
scratchpads (instead of caches) for the compute-heavy processing units. This
meant that data (and even instructions!) had to be explicit copied in and out
of the memories of the individual processors, something which Sequoia excelled
at. Unfortunately, Roadrunner ended up not being so representative of the
machines that would follow, and the effort required to retool all the
infrastructure ended up being a big drain on the project. Though it would not
surprise anyone if these ideas ended up resurfacing in the future, a project
like this simply can't afford not to work on contemporary hardware.

In contrast, Legion made its bet on GPUs and heterogeneous architectures more
generally. So far this appears to be turning out well and has positioned us to
work well on architectures such as
[Summit](https://en.wikipedia.org/wiki/Summit_(supercomputer)). However,
investment in technologies such as LLVM mean that we're also in a better
position to adapt if future technologies end up going in a different
direction.