jank development update - Optimizing a ray tracer
Apr 07, 2023 · Jeaye Wilkerson

After the last post, which focused on optimizing jank's sequences, I wanted to get jank running a ray tracer I had previously written in Clojure. In this post, I document what was required to start ray tracing in jank and, more importantly, how I chased down the run time in a fierce battle with Clojure's performance.

Missing Clojure functions

Coming out of the last blog post, there were quite a few functions which the ray tracer required that jank did not yet have. A lot of this was tedium, but there are some interesting points.

Polymorphic arithmetic

In Clojure JVM, since everything can be an object, and Clojure's dynamically typed, we can't know what something like (+ a b) actually does. For example, it's possible that either a or b is not a number, but it's also possible that they're an unboxed long or double, or a boxed Long or a Double, or maybe even a BigInteger or Ratio. Each of these will handle + slightly differently. Clojure (and now jank) handles this using a neat polymorphic design. In jank, it starts with this number_ops interface:

struct number_ops
  virtual number_ops const& combine(number_ops const&) const = 0;
  virtual number_ops const& with(integer_ops const&) const = 0;
  virtual number_ops const& with(real_ops const&) const = 0;

  virtual object_ptr add() const = 0;
  virtual object_ptr subtract() const = 0;
  virtual object_ptr multiply() const = 0;
  virtual object_ptr divide() const = 0;
  virtual object_ptr remainder() const = 0;
  virtual object_ptr inc() const = 0;
  virtual object_ptr dec() const = 0;
  /* ... and so on ... */

jank then has different implementations of this interface, like integer_ops and real_ops. The trick here is to use the correct "ops" for the combination of left and right. By left and right, I mean, when looking at the expression (+ a b), we see the left side, a, and the right side, b. So if they're both integers, we can use the integer_ops, which returns more integers. But if one is an integer and the other is a real, we need to return a real. You can see this in Clojure, since (+ 1 2) is 3, but (+ 1 2.0) is 3.0.

The way this comes together is something like this:

object_ptr add(object_ptr const l, object_ptr const r)
{ return with(left_ops(l), right_ops(r)).add(); }

You can see the Clojure source for this here and the jank source for this here.

Running the ray tracer

This ray tracer is a partial port of the very fun Ray tracing in one weekend project. It's not meant to be fast; it's a learning tool. However, it was a pure Clojure project I had lying around and seemed like a good next goal for jank. The scene is mostly random, but generally look like this:

The source code for both the jank and the Clojure versions are in this gist. They vary only in the math functions used; each one uses its host interop for them. Note that this is not the most idiomatic Clojure; it was written to work with the limitations of a previous iteration of jank, then somewhat upgraded. jank still doesn't support some idiomatic things like using keywords as functions, so there are many calls to get.

After giving that code a tolerant scan, let's take a look at the initial numbers.

First timing results

I'm ray tracing a tiny image. It's only 10x6 pixels. However, in order to create this tiny image, we need to cast 265 rays. Here's the image:

Now, the initial timing for this (using nanobench) for jank is here:


Just shy of 800 milliseconds. It's less than a second, yes, but it's also a trivially tiny image. Let's see how Clojure does with the same code (using criterium).

; (out) Evaluation count : 12 in 6 samples of 2 calls.
; (out)              Execution time mean : 69.441424 ms
; (out)     Execution time std-deviation : 8.195639 ms
; (out)    Execution time lower quantile : 63.812357 ms ( 2.5%)
; (out)    Execution time upper quantile : 83.135203 ms (97.5%)
; (out)                    Overhead used : 11.626368 ns

Yikes. Clojure only takes 69 ms. jank takes 11.6x as long as Clojure to render the same image. That's ok, though. We didn't want this to be easy.

Profile, change, benchmark, repeat

My general understanding of the ray tracer was that we would spend most of our time:

  • Crunching numbers (using that polymorphic arithmetic I outlined above)
  • Creating maps (for rays, mainly)
  • Looking up data in maps (for rays, mainly)

Keywords with identity for equal and hash

Going into this, I knew one thing I could tackle right out of the gate. Map lookups were just using equality checks, but keywords can be compared using identity (pointer value), since they're interned.

The changes: 067e0ee400bffea010cf8bfad0e81a75134f7771


Very nice. We're coming out swinging. From 797.49 ms to 559.31 ms is a big win.

Compare generated code

After this, I didn't have other optimizations planned, so the next reasonable step was to just take a look at the generated code from both jank and Clojure and try to spot some differences. For decompiling Clojure code, I'll use the excellent clj-java-decompiler.

Given the three expected time sinks I listed above, I chose a function which had map creation, map lookups, and arithmetic. Should be the exact sort of thing we need to optimize. Here's the function:

(defn vec3-scale [l n]
  {:r (* (get l :r) n)
   :g (* (get l :g) n)
   :b (* (get l :b) n)})

Let's see the jank code for this first. I've annotated it for readability.

/* Our function was turned into a struct which implements
   some jank interfaces. */
struct vec3_scale579 : jank::runtime::object,
                       jank::runtime::behavior::metadatable {
  jank::runtime::context &__rt_ctx;

  /* Vars referenced within the fn are lifted to members.
     In this case, * and get. */
  jank::runtime::var_ptr const _STAR_595;
  jank::runtime::var_ptr const get596;

  /* Constants are lifted to members. */
  jank::runtime::object_ptr const const600;
  jank::runtime::object_ptr const const594;
  jank::runtime::object_ptr const const598;

  /* Constructor which initializes all lifted vars and constants. */
  vec3_scale579(jank::runtime::context &__rt_ctx)
      : __rt_ctx{__rt_ctx},
        _STAR_595{__rt_ctx.intern_var("clojure.core", "*").expect_ok()},
        get596{__rt_ctx.intern_var("clojure.core", "get").expect_ok()},
        const600{__rt_ctx.intern_keyword("", "b", true)},
        const594{__rt_ctx.intern_keyword("", "r", true)},
        const598{__rt_ctx.intern_keyword("", "g", true)}
  { }

  /* This is where the actual jank code we wrote runs. */
  jank::runtime::object_ptr call
    jank::runtime::object_ptr l,
    jank::runtime::object_ptr n
  ) const override {
    /* First we call (* (get l :r) n). We can see the calls to get and * here. */
    object_ptr call607;
      object_ptr call608;
      { call608 = jank::runtime::dynamic_call(get596->get_root(), l, const594); }
      call607 = jank::runtime::dynamic_call(_STAR_595->get_root(), call608, n);

    /* Same thing for calling (* (get l :g) n). */
    object_ptr call609;
      object_ptr call610;
      { call610 = jank::runtime::dynamic_call(get596->get_root(), l, const598); }
      call609 = jank::runtime::dynamic_call(_STAR_595->get_root(), call610, n);

    /* Same thing for calling (* (get l :b) n). */
    object_ptr call611;
      object_ptr call612;
      { call612 = jank::runtime::dynamic_call(get596->get_root(), l, const600); }
      call611 = jank::runtime::dynamic_call(_STAR_595->get_root(), call612, n);

    /* Finally, we create a map from all of this and return it. */
    auto const map613
      (std::in_place, const594, call607, const598, call609, const600, call611)
    return map613;

No big surprises there. Each var is dereferenced (with ->get_root()) when it's used, since vars can change at any time, from any thread. Let's see what Clojure generates for the same function.

/* Just like in jank, a class was generated for this function. */
public final class core$vec3_scale extends AFunction
    /* Just like in jank, the constants were lifted to be members. */
    public static final Keyword const__0;
    public static final Keyword const__3;
    public static final Keyword const__4;
    public static Object invokeStatic(final Object l, final Object n)
      /* Wait... no var derefs. Clojure didn't generate calls to clojure.core/*
         at all. It replaced them with calls to `Numbers.multiply`. Same with
      return RT.mapUniqueKeys
        const__0, Numbers.multiply(RT.get(l, const__0), n),
        const__3, Numbers.multiply(RT.get(l, const__3), n),
        const__4, Numbers.multiply(RT.get(l, const__4), n)
    public Object invoke(final Object l, final Object n)
    { return invokeStatic(l, n); }
    /* We initialize the constants here, instead of in the function's
       constructor. This is a small performance win, since it only needs to
       happen once. */
    static {
      const__0 = RT.keyword(null, "r");
      const__3 = RT.keyword(null, "g");
      const__4 = RT.keyword(null, "b");

Ok, that's cheating! Clojure skipped past the actual implementation of clojure.core/* and generated something else in its place. This not only skips the var dereference, but it allows for calling right into a Java function which is going to be faster than something written in Clojure. It also skipped past the var for clojure.core/get and instead generated in a call to RT.get. While what I said about vars changing all the time is true, Clojure makes an exception for some functions within clojure.core.

Fine. I can cheat, too. The changes:

But before we look at the new time, there was another thing Clojure is doing. Clojure unboxes numbers whenever possible. Unboxing means to use automatic memory, rather than dynamic memory for storing something; this is frequently referred to as using "the stack". Take a look at this function here:

(defn reflectance [cosine ref-idx]
  (let [r (/ (- 1.0 ref-idx)
             (+ 1.0 ref-idx))
        r2 (* r r)]
    (* (+ r2 (- 1.0 r2))
       (Math/pow (- 1.0 cosine) 5.0))))

Because (- 1.0 ref-idx) contains a double, Clojure can know that the whole expression will return a double. By then tracking how r is used, we can see if it requires boxing at all. In this case, r is only used with *, which doesn't require boxing. So r can actually just be a double instead of a Double. The same applies for everything else. Take a look at the generated code.

public final class core$reflectance extends AFunction
  public static Object invokeStatic(final Object cosine, final Object ref_idx)
    final double r = Numbers.minus(1.0, ref_idx) / Numbers.add(1.0, ref_idx);
    final double r2 = r * r;
    return Numbers.multiply(r2 + (1.0 - r2), Math.pow(Numbers.minus(1.0, cosine), 5.0));

One last point about this: you may note that we're not boxing the double we're returning. The call to Numbers.multiply with two double inputs returns a double, but our function returns an Object. This works because Java supports auto-boxing and unboxing. In short, it will allow you to implicitly treat boxed and unboxed objects the same, injecting in the necessary code when it compiles. So, don't be fooled, the final return value here is boxed.

To do a similar thing in jank, I broke it into some steps:

Generate more type info

Changes: 3e18c1025f3a6db2028d55f819594208197e1a78

The goal here is to use boxed types (integer_ptr, keyword_ptr, etc) during codegen, whenever we have them, rather than just object_ptr everywhere. Also, whenever possible, use auto to allow the propagation of richer types.

Add boxed typed overloads for math fns

Changes: 083f08374dd371dacf6b854decbada83d336fbd6

Even without unboxing, if we can know we're adding a real_ptr and a real_ptr, for example, we can skip the polymorphic dance and get right at their internal data. We don't do that yet, here, but we add the right overloads.

Extend the codegen to convey, for any expression, whether a box is needed

Changes: 626680ecdb09d9007378e5f17bd06f8bcfd285ff

This sets the stage for unboxed math, if conditions, let bindings, etc.

Remove polymorphism from boxed math ops, when possible

Changes: 87715f9a711e0e0bd667afce221ccbe4a85b5501

This utilizes the typed overloads to optimize the calls where we have a typed box like integer_ptr or real_ptr.

Add unboxed math overloads

Changes: d6d97e9a58f6a40dcc425b8da80c9bd69faa0886

Wrapping everything together, this allows expressions like (/ 1.0 (+ n 0.5)) to avoid boxing, at least for the (+ n 0.5). Conditions for if don't require boxing, so something like (if (< y (/ 1.0 (+ n 0.5))) ...) wouldn't box at all.

I didn't implement unboxed let bindings, since that requires tracking binding usages to know if each one requires a box and I'm lazy. Still, let's see what we won.


Nice! That was a lot of work, but it gets us down from 559.31 ms to 312.78 ms.

Profile results

At this point, inspecting the generated code wasn't showing any clear opportunities, so I profiled again and looked for the primary culprits. They were:

  • get
  • mul, add, sub
  • map constructor

Hm, strikingly similar to the expectations set when we started. Sometimes it's unfortunate not to find surprises. Still, let's see what we can do about those maps.

Faster map

jank's array map implementation was using a vector of pairs. Note that both jank and Clojure distinguish between array maps and hash maps. Array maps are a specialization for short maps (i.e. few keys) which don't have the overhead of trees. Rather than O(log32 n) access with some constant overhead, they have O(n) access with very little overhead. When n is small enough, it ends up being faster. Once array maps get too big, they convert automatically into hash maps. For this ray tracer, all the maps are very small, so we're only looking at the array map implementation. jank's array map looked like this:

template <typename K, typename V>
struct map_type_impl
  /* Storing a vector of key/value pairs. */
  using value_type = native_vector<std::pair<K, V>>;
  using iterator = typename value_type::iterator;
  using const_iterator = typename value_type::const_iterator;

  map_type_impl() = default;
  map_type_impl(map_type_impl const &s) = default;
  map_type_impl(map_type_impl &&s) noexcept = default;
  map_type_impl(in_place_unique, value_type &&kvs)
    : data{ std::move(kvs) }
  { }
  ~map_type_impl() = default;

  /* ... insert fns ... */

  /* Note the linear search. */
  V find(K const &key) const
    if(auto const kw = key->as_keyword())
      for(auto const &kv : data)
        if(kv.first == key)
        { return kv.second; }
      for(auto const &kv : data)
        { return kv.second; }
    return nullptr;

  /* ... hashing, iterators, size, etc ... */

  value_type data;
  mutable size_t hash{};

This is using folly's vector, a more optimized version of std::vector. Turns out it's still very slow to create, compared to just an array. I ended up benchmarking both vector types, std::array, and finally C arrays, each with pairs and without. C arrays, without pairs, clearly won. Since there are no pairs, keys and values are interleaved. Turns out this is exactly what Clojure does in PersistentArrayMap.java.

The new array map is quite similar:

template <typename KV>
struct map_type_impl
  /* Just a C array and a length. */
  using value_type = KV*;

  map_type_impl() = default;
  map_type_impl(map_type_impl const &s) = default;
  map_type_impl(map_type_impl &&s) noexcept = default;
  map_type_impl(in_place_unique, value_type const kvs, size_t const length)
    : data{ kvs }, length{ length }
  { }
  ~map_type_impl() = default;

  /* ... insert fns ... */

  KV find(KV const key) const
    if(auto const kw = key->as_keyword())
      /* Interleaved key/values changes iteration. */
      for(size_t i{}; i < length; i += 2)
        if(data[i] == key)
        { return data[i + 1]; }
      for(size_t i{}; i < length; i += 2)
        { return data[i + 1]; }
    return nullptr;

  /* Custom iteration is needed, due to the interleaving. */
  struct iterator
    using iterator_category = std::input_iterator_tag;
    using difference_type = std::ptrdiff_t;
    using value_type = std::pair<KV, KV>;
    using pointer = value_type*;
    using reference = value_type&;

    value_type operator *() const
    { return { data[index], data[index + 1] }; }
    iterator& operator ++()
      index += 2;
      return *this;
    bool operator !=(iterator const &rhs) const
    { return data != rhs.data || index != rhs.index; }
    bool operator ==(iterator const &rhs) const
    { return !(*this != rhs); }
    iterator& operator=(iterator const &rhs)
      if(this == &rhs)
      { return *this; }

      data = rhs.data;
      index = rhs.index;
      return *this;

    KV const* data{};
    size_t index{};
  using const_iterator = iterator;

  value_type data{};
  size_t length{};
  mutable size_t hash{};

Let's take a look at the new numbers.


From 312.78 ms to 199.31 ms. This actually optimized both map creation and map lookup. We're still in the 3x territory, compared to Clojure, though. We need some more big wins.

Compiler flags

Big wins in the code were getting tough to come by, at this point, so I started looking elsewhere. What flags are we using for the compiler? What about the JIT compiler? We had -O1 for both, due to some Cling issues which prevented me from going higher. I spent some time working around those and was able to get jank compiling with -O3. This alone was a huge win, but I was able to tweak Cling's flags as well, to balance the trade-off between run time and compile time. We can't go to -O3 for Cling, but we can do better than just -O1. That can mean -ffast-math, -march=native, etc. More importantly, we can make this configurable with a command line flag.


Very nice. This gets us within the 2x range from Clojure, so it's time to pull out a card I've been hanging onto for a while.

Disable incremental GC

I've been running all of this with Boehm's GC set to incremental mode, which actually does some garbage collection on every allocation. This is sometimes preferable for more deterministic frame rate, for example, but jank needs to provide this as a run-time option rather than hard-coding it. The default Boehm GC mode, which is what the JVM is doing for Clojure, collects far less frequently, but it "stops the world" for longer in order to do so. If we switch back to that mode:


Oh yeah. We're about 4 ms away from beating Clojure and we haven't changed a single line of the ray tracer.

Final stretch

I tried a lot of things here. Profiling wasn't giving me much, mostly due to issues with Cling, which don't allow me to profile with optimizations above -O1. Though I'm confident that unboxing those let bindings would likely do the trick, I was still too lazy, so I looked elsewhere.

Starting at 73.07 ms, I ran through some optimizations I could see:

  1. Use final everywhere, to encourage devirtualization (changes): 72.32 ms
  2. Unbox constants wherever possible (changes): 70.72 ms
  3. Unboxed, direct fn calls when possible (changes): no change
  4. Unboxed integer_range instead of polymorphic range (didn't commit): no change
  5. Remove excess option<T> use, showing up in profiler (changes): no change

Finally, I noticed through more careful profiling that reduce* was showing up. Let's see how it was written.

(defn reduce*
  ; ... Ignoring the binary version ...
  ([f val coll]
    (let [s (seq coll)]
      (if s
        (recur f (f val (first s)) (next s))

Ah, this was one of the few jank functions in clojure.core which didn't yet have a native implementation. Also, let's take a look at some key points here:

  1. Each iteration, we call seq on coll
  2. We call next on the sequence, since we don't have next-in-place exposed in jank; this would only be safe if we know it's a fresh sequence, though

In the last optimization post, we added the next_in_place function to the sequence object, which allows us to iterate without another allocation. By default, Clojure's sequences allocate a new seq for each iteration. But this in-place update can only be done if we're 100% certain we have a fresh sequence which nobody else is hanging onto. Right now, this can be known in three ways:

  1. Implicitly, if we're in a variadic function, since we know that the packed args will be a sequence created just for this function
  2. Also implicitly, if we have the result of calling next on a sequence, which is required to return a fresh sequence
  3. Explicitly, if we just made the sequence ourselves

I think we need another explicit way to do this and that we should remove the second implicit way by allowing next to not return fresh sequences. This would allow a range to pre-allocate sequences, for example. So, I added a fresh_seq function to the seqable behavior, which must explicitly return a fresh sequence which can then be updated in place. Both fresh-seq and next-in-place were added to clojure.core, too, though they may end up in a different namespace eventually. The new version of reduce* only has one allocation (the fresh seq), regardless of the size of coll, and looks like this:

(defn reduce*
  ; ... Ignoring the binary version ...
  ([f val coll]
    (native/raw "__value = #{ val }#;
                   auto it(#{ coll }#->as_seqable()->fresh_seq());
                   it != nullptr;
                   it = it->next_in_place()
                 { __value = dynamic_call(f, __value, it->first()); }")))

The changes: 93626ac79919d9280985e90746fbc229554e5788


Phew. From 70.72 ms down to 69.00 ms with this change. We have successfully beat Clojure's 69.44 ms, albeit by less than half of a millisecond.


jank is getting faster and faster and it's also getting more and more functionality. Still, I need to note some things. This was all benchmarked in a REPL. Clojure supports AOT compilation, direct linking, type hints, etc, which would make it even faster. I will benchmark against those when jank supports them. For this exercise, I was only focused on how Clojure and jank ran the same code out of the box.

There's a lot more which jank could do in order to drop this time further. We already mentioned tracking let bindings, so we could unbox them. Each jank-defined function, right now, is limited to accepting object_ptr inputs and returning one object_ptr output. Also, my benchmarking has shown that trying to mimic Clojure's Java hierarchy in C++ is just not going to pan out well, so a different solution will be required. Early prototyping of that has been very promising, but more will be shared in a future post.

I'm confident jank can consistently be faster than Clojure, and I will work to make that happen. But I also realize that speed is only one metric; jank needs to be easily usable, be as simple as possible, have great tooling, and embrace the culture of stability which Clojure has. Future posts will also be focusing on these metrics.

Would you like to join in?

  1. Join the community on Slack
  2. Join the design discussions on GitHub
  3. Considering becoming a Sponsor