I’ve long held that any programming language worth its salt should provide two fundamental data structures:

  • an integer-indexable collection (think Array in sclang)
  • an associative key-value dictionary-style container supporting amortized constant-time access.

I think I can trace this back to working on a large Python code base comparatively early in my career. Coming from C, I initially found Python’s lack of pointer arithmetic limiting. Eventually, Python taught me one could make any arbitrary data structure with those two fundamental elements.

When learning any new programming language, I look for those two primitives. So, when I started to learn SuperCollider, I found Array and IdentityDictionary. As I’ve been working on implementing Hadron, I’ve built all of the sclang-accessible data structures from those two components. Recently, I implemented Hadron’s version of the IdentityDictionary. Because of Hyrum’s Law, I want to ensure the Hadron version behaves similarly to the existing sclang implementation. So, we will first study that.

SCLang IdentityDictionary Implementation

Let’s take a look at how IdentityDictionary looks up values given a key:

	at { arg key;
		/*^array.at(this.scanFor(key) + 1)*/

Well, that’s promising. The _IdentDict_At token identifies a primitive in sclang, meaning an instruction to the sclang interpreter to call a C++ function connected to that keyword and compiled into the interpreter binary. Primitives are a common idiom in sclang library code, particularly for methods supporting core data structures like IdentityDictionary. I’ve seen them used for a variety of reasons in sclang:

  • when manipulating language internals or changing values that aren’t accessible from sclang
  • connecting to other C/C++ libraries that don’t have a sclang interface (e.g., MIDI, HID)
  • when the language implementor wants to leverage the additional speed compiled C++ code offers over sclang

We want to read the C++ code associated with the _IdentDict_At primitive. I usually search the lang/ subdirectory of the SuperCollider repository for the primitive token:

lang % grep -Rn _IdentDict_At *
LangPrimSource/PyrListPrim.cpp:785:    definePrimitive(base, index++, "_IdentDict_At", prIdentDict_At, 2, 0);

Once you search for a few of these you’ll find that they usally reside in the LangPrimSource subdirectory and are associated with a definePrimitive() call just like this one. The argument following the primitive name is the associated function name, prIdentDict_At, and grepping for that function name reveals the function definition in the same file, PyrListPrim.cpp:

int prIdentDict_At(struct VMGlobals* g, int numArgsPushed) {
    PyrSlot* a = g->sp - 1; // dict
    PyrSlot* key = g->sp; // key
    PyrObject* dict = slotRawObject(a);

    /* SNIP */

    identDict_lookup(dict, key, calcHash(key), a);
    return errNone;

We start the function by extracting the arguments from the sclang stack inside the VMGlobals structure g->sp (or stack pointer). The code expects an IdentityDictionary at sp - 1 and the key to lookup at sp. SuperCollider stacks grow upward, meaning the stack pointer is incremented on a push, so this corresponds to the caller pushing the dictionary pointer, followed by the key.

I’ve omitted some code dealing with additional logic IdentityDictionary offers around the knows member. The relevant code for our research is the identDict_lookup call.

Here’s the code for the first part of identDict_lookup, also excerpted from PyrListPrim.cpp:

bool identDict_lookupNonNil(PyrObject* dict, PyrSlot* key, int hash, PyrSlot* result) {
    PyrSlot* dictslots = dict->slots;
    PyrSlot* arraySlot = dictslots + ivxIdentDict_array;

    if (isKindOfSlot(arraySlot, class_array)) {
        PyrObject* array = slotRawObject(arraySlot);

        int index = arrayAtIdentityHashInPairsWithHash(array, key, hash);
        if (SlotEq(key, array->slots + index)) {
            slotCopy(result, &array->slots[index + 1]);
            return true;

    /* SNIP */

I’ve omitted further knows hijinks from the bottom half of the function. We follow the code to the call to arrayAtIdentityHashInPairsWithHash(..), which returns an index to the element matching hash in the array member of IdentityDictionary, which it inherited from Set. The SlotEq check is to see if the key matches the one supplied in the argument. If the keys match, it copies the next element in the array into the result buffer and returns.

Now we’re getting somewhere. The name arrayAtIdentityHashInPairsWithHash, and the access pattern index + 1, implies that IdentityDictionary organizes its data as key/value pairs in adjacent elements in an internal array. Let’s complete the journey and look at the actual lookup code, still in the same PyrListPrim.cpp:

int arrayAtIdentityHashInPairsWithHash(PyrObject* array, PyrSlot* key, int hash) {
    PyrSlot *slots, *test;
    unsigned int i, start, end, maxHash;

    maxHash = array->size >> 1;
    start = (hash % maxHash) << 1;
    end = array->size;
    slots = array->slots;
    for (i = start; i < end; i += 2) {
        test = slots + i;
        if (IsNil(test) || SlotEq(test, key))
            return i;
    end = start - 2;
    for (i = 0; i <= end; i += 2) {
        test = slots + i;
        if (IsNil(test) || SlotEq(test, key))
            return i;
    return -2;

This code is terse and uncommented, requiring some careful reading. The algorithm uses hash to identify a position in the array to start its search. If the element associated with key is the only element in the dictionary, then it would be stored at the position calculated for start. The hash is a 32-bit signed number, addressing roughly 2 billion positive values, so we can’t use it directly as the index for key. So the code computes the modulus of hash on the size of the current array, using only the least significant bits of hash needed to derive the index of key. The shifting right and left with >> and << divides and then multiplies the values by two, accounting for the elements in the arrays in key/value pairs.

Taking the modulus of the hash is a classic technique of hash tables that allows indexing tractably-sized arrays. Using part of the key can result in collisions, meaning two different hashes can map to the same start value. The code probes the array, checking each value in order from the start. If it encounters an empty slot or match, the function returns that index immediately.

To insert a key, we perform the identical index computation and probing behavior, inserting the new element in the first open position the algorithm finds. So, it makes sense that the searching code here would follow the same pattern. The key is not in the dictionary if it encounters an empty element while probing.

I spent a few minutes trying to deduce the meaning of the -2 return value on the final line. I think it would cause the interpreter to crash if ever executed. The calling code uses that returned index with the assumption that it is a valid index in the array, so calling SlotEq on a pointer 32 bytes (or two 16-byte slots) before the start of the array elements seems like a buffer underrun and possible segmentation fault. The crash would occur for any call of this function on a full dictionary (so, no null entries) and a key not in the dictionary. I suspect that it never gets executed because of the resizing logic applied within its mirror function, identDictPut:

int identDictPut(struct VMGlobals* g, PyrObject* dict, PyrSlot* key, PyrSlot* value) {

    /* SNIP */

    index = arrayAtIdentityHashInPairs(array, key);
    slot = array->slots + index;
    slotCopy(&slot[1], value);
    g->gc->GCWrite(array, value);
    if (IsNil(slot)) {
        slotCopy(slot, key);
        g->gc->GCWrite(array, key);
        size = slotRawInt(&dict->slots[ivxIdentDict_size]) + 1;
        SetRaw(&dict->slots[ivxIdentDict_size], size);
        if (array->size < size * 3) {
            PyrObject* newarray;
            newarray = newPyrArray(g->gc, size * 3, 0, false);
            newarray->size = ARRAYMAXINDEXSIZE(newarray);
            nilSlots(newarray->slots, newarray->size);
            slot = array->slots;
            for (i = 0; i < array->size; i += 2, slot += 2) {
                if (NotNil(slot)) {
                    index = arrayAtIdentityHashInPairs(newarray, slot);
                    newslot = newarray->slots + index;
                    slotCopy(&newslot[0], &slot[0]);
                    slotCopy(&newslot[1], &slot[1]);
            SetRaw(&dict->slots[ivxIdentDict_array], newarray);
            g->gc->GCWriteNew(dict, newarray); // we know newarray is white so we can use GCWriteNew
    return errNone;

I’ve cut out the top part of the function. The salient bits here are that it looks for an index in the current array, copies the value into place in the slot following the key, then checks if it should also copy the key. The check for IsNil(slot) determines if this put operation is overwriting an existing value previously associated with the key. If the slot is nil this is a new value, so the following code copies the key and then increases the size element by one.

The following if size comparison is interesting. The array stores two elements (key and value) for each Dictionary entry. This code resizes the array if it doesn’t have room for triple the size. This strategy ensures that there will always be empty slots within the dictionary, leaving that -2 crash bug to sleep in dead code for another day.

The hash table strategy used here is classically called Open Addressing with Linear Probing. This style of hash table favors cache access at the expense of a possible “clustering” problem; when entries clump up in the table next to each other due to the linear probing behavior of inserting colliding elements into the next available slot in the storage array. Clustering worsens as the hash table fills up, which explains the code in SCLang to limit the IdentityDictionary to two-thirds full.

Note the re-hash happening after the resize. Resizing the array changes the index for every key in the hash table, so each element is re-hashed during insertion.

Next Steps

I took what I learned from this study and implemented Hadron’s IdentityDictionary primitives for insertion and key lookup. Long term, I’m not confident that the specific trade-offs made in the current implementation work well for Hadron. While I haven’t completed the design yet, I anticipate that Hadron’s garbage collector will be very different from the one inside SCLang, resulting in new options in terms of data structure design and performance characteristics.

I’ve tried to avoid premature optimization while working on Hadron; outside of some fundamental architecture choices (e.g., JIT) I’d say I’ve been mostly successful. I’m still in pursuit of correctness first. While implementing major subsystems, I’ve had to change them enough that I’m confident I would have wasted a lot of early optimization work had I decided to pause on the correctness effort and go and optimize. So, mostly this has felt like the right call.

However, in implementing these data structures, I must admit that I struggled mightily against the temptation to redesign them. I will eventually implement a HadronHashTable, and likely will migrate all of the Hadron language data structures that currently use IdentityDictionary to that. I want to think I’ll wait until I have overall performance data that suggests a data structure redesign would be the most impactful optimizations I could make on Hadron, but I might not be that disciplined.

Getting to correctness seems like a ton of work, making this optimization work feel even further away. My intuition suggests that dictionary optimizations would be impactful, particularly considering that the Event class, central to many SuperCollider pattern-based workflows, derives from IdentityDictionary. Time will tell.