WebAssembly Multi-Value Function Call Convention: Unlocking Parameter Passing Optimization

In the rapidly evolving landscape of web development and beyond, WebAssembly (Wasm) has emerged as a cornerstone technology. Its promise of near-native performance, safe execution, and universal portability has captivated developers worldwide. As Wasm continues its journey of standardization and adoption, crucial proposals enhance its capabilities, driving it closer to fulfilling its full potential. One such pivotal enhancement is the Multi-Value proposal, which fundamentally redefines how functions can return and accept multiple values, leading to significant parameter passing optimizations.

This comprehensive guide delves into the WebAssembly Multi-Value Function Call Convention, exploring its technical underpinnings, the profound performance benefits it introduces, its practical applications, and the strategic advantages it offers to developers across the globe. We will contrast the "before" and "after" scenarios, highlighting the inefficiencies of previous workarounds and celebrating the elegant solution multi-value provides.

The Foundations of WebAssembly: A Brief Overview

Before we embark on our deep dive into multi-value, let's briefly revisit the core tenets of WebAssembly. Wasm is a low-level bytecode format designed for high-performance applications on the web and various other environments. It operates as a stack-based virtual machine, meaning instructions manipulate values on an operand stack. Its primary goals are:

• Speed: Near-native execution performance.
• Safety: A sandboxed execution environment.
• Portability: Runs consistently across different platforms and architectures.
• Compactness: Small binary sizes for faster loading.

Wasm's fundamental data types include integers (i32, i64) and floating-point numbers (f32, f64). Functions are declared with specific parameter and return types. Traditionally, a Wasm function could only return a single value, a design choice that, while simplifying the initial specification, introduced complexities for languages that naturally handle multiple return values.

Understanding Function Call Conventions in Wasm (Pre-Multi-Value)

A function call convention defines how arguments are passed to a function and how return values are received. It's a critical agreement between the caller and the callee, ensuring they understand where to find parameters and where to place results. In the early days of WebAssembly, the call convention was straightforward but limited:

• Parameters are pushed onto the operand stack by the caller.
• The function body pops these parameters from the stack.
• Upon completion, if the function has a return type, it pushes a single result onto the stack.

This single-return-value limitation posed a significant challenge for source languages like Rust, Go, or Python, which frequently allow functions to return multiple values (e.g., (value, error) pairs, or multiple coordinates (x, y, z)). To bridge this gap, developers and compilers had to resort to various workarounds, each introducing its own set of overheads and complexities.

The Costs of Single-Value Return Workarounds:

Before the Multi-Value proposal, returning multiple logical values from a Wasm function necessitated one of the following strategies:

1. Heap Allocation and Pointer Passing:

The most common workaround involved allocating a block of memory (e.g., a struct or a tuple) in the Wasm module's linear memory, populating it with the desired multiple values, and then returning a single pointer (an i32 or i64 address) to that memory location. The caller would then have to dereference this pointer to access the individual values.

• Overhead: This approach incurs significant overhead from memory allocation (e.g., using malloc-like functions within Wasm), memory deallocation (free), and the cache penalties associated with accessing data via pointers rather than directly from the stack or registers.
• Complexity: Managing memory lifetimes becomes more intricate. Who is responsible for freeing the allocated memory? The caller or the callee? This can lead to memory leaks or use-after-free bugs if not handled meticulously.
• Performance Impact: Memory allocation is an expensive operation. It involves searching for available blocks, updating internal data structures, and potentially fragmenting memory. For frequently called functions, this repeated allocation and deallocation can severely degrade performance.

2. Global Variables:

Another, less advisable, approach was to write the multiple return values into global variables visible within the Wasm module. The function would then return a simple status code, and the caller would read the results from the globals.

• Overhead: While avoiding heap allocation, this approach introduces challenges with reentrancy and thread safety (though Wasm's threading model is still evolving, the principle applies).
• Limited Scope: Globals are not suitable for general-purpose function returns due to their module-wide visibility, making code harder to reason about and maintain.
• Side Effects: Reliance on global state for function returns obfuscates the function's true interface and can lead to unexpected side effects.

3. Encoding into a Single Value:

In very specific, limited scenarios, multiple small values could be packed into a single larger Wasm primitive. For instance, two i16 values could be packed into a single i32 using bitwise operations, and then unpacked by the caller.

• Limited Applicability: This is only feasible for small, compatible types and doesn't scale.
• Complexity: Requires additional packing and unpacking instructions, increasing instruction count and potential for errors.
• Readability: Makes the code less clear and harder to debug.

These workarounds, while functional, undermined Wasm's promise of high performance and elegant compilation targets. They introduced unnecessary instructions, increased memory pressure, and complicated the compiler's task of generating efficient Wasm bytecode from high-level languages.

The Evolution of WebAssembly: Introducing Multi-Value

Recognizing the limitations imposed by the single-value return convention, the WebAssembly community actively developed and standardized the Multi-Value proposal. This proposal, now a stable feature of the Wasm specification, allows functions to declare and handle an arbitrary number of parameters and return values directly on the operand stack. It's a fundamental shift that brings Wasm closer to the capabilities of modern programming languages and host CPU architectures.

The core concept is elegant: instead of being limited to pushing one return value, a Wasm function can push multiple values onto the stack. Similarly, when calling a function, it can consume multiple values from the stack as arguments and then receive multiple values back, all directly on the stack without intermediate memory operations.

Consider a function in a language like Rust or Go that returns a tuple:

            // Rust example
fn calculate_coordinates() -> (i32, i32) {
    (10, 20)
}

// Go example
func calculateCoordinates() (int32, int32) {
    return 10, 20
}
            

              
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Before multi-value, compiling such a function to Wasm would involve creating a temporary struct, writing 10 and 20 into it, and returning a pointer to that struct. With multi-value, the Wasm function can directly declare its return type as (i32, i32) and push both 10 and 20 onto the stack, exactly mirroring the source language's semantics.

The Multi-Value Call Convention: A Deep Dive into Parameter Passing Optimization

The introduction of the Multi-Value proposal revolutionizes the function call convention in WebAssembly, leading to several critical parameter passing optimizations. These optimizations directly translate into faster execution, reduced resource consumption, and simplified compiler design.

Key Optimization Benefits:

1. Elimination of Redundant Memory Allocation and Deallocation:

This is arguably the most significant performance gain. As discussed, prior to multi-value, returning multiple logical values typically required dynamic memory allocation for a temporary data structure (e.g., a tuple or struct) to hold these values. Each allocation and deallocation cycle is expensive, involving:

• System Calls/Runtime Logic: Interacting with the Wasm runtime's memory manager to find an available block.
• Metadata Management: Updating internal data structures used by the memory allocator.
• Cache Misses: Accessing newly allocated memory can lead to cache misses, forcing the CPU to fetch data from slower main memory.

With multi-value, parameters are passed and returned directly on the Wasm operand stack. The stack is a highly optimized memory region, often residing entirely or partially within the CPU's fastest caches (L1, L2). Stack operations (push, pop) are typically single-instruction operations on modern CPUs, making them incredibly fast and predictable. By avoiding heap allocations for intermediate return values, multi-value drastically reduces execution time, especially for functions called frequently in performance-critical loops.

2. Reduced Instruction Count and Simplified Code Generation:

Compilers targeting Wasm no longer need to generate complex instruction sequences for packaging and unpackaging multiple return values. For instance, instead of:

            (local.get $value1)
(local.get $value2)
(call $malloc_for_tuple_of_two_i32s)
(local.set $ptr_to_tuple)
(local.get $ptr_to_tuple)
(local.get $value1)
(i32.store 0)
(local.get $ptr_to_tuple)
(local.get $value2)
(i32.store 4)
(local.get $ptr_to_tuple)
(return)
            

              
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The multi-value equivalent can be much simpler:

            (local.get $value1)
(local.get $value2)
(return) ;; Returns both values directly
            

              
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This reduction in instruction count means:

• Smaller Binary Size: Less generated code contributes to smaller Wasm modules, leading to faster downloads and parsing.
• Faster Execution: Fewer instructions to execute per function call.
• Easier Compiler Development: Compilers can map high-level language constructs (like returning tuples) more directly and efficiently to Wasm, reducing the complexity of the compiler's intermediate representation and code generation phases.

3. Enhanced Register Allocation and CPU Efficiency (at the Native Level):

While Wasm itself is a stack machine, underlying Wasm runtimes (like V8, SpiderMonkey, Wasmtime, Wasmer) compile Wasm bytecode to native machine code for the host CPU. When a function returns multiple values on the Wasm stack, the native code generator can often optimize this by mapping these return values directly to CPU registers. Modern CPUs have multiple general-purpose registers that are significantly faster to access than memory.

• Without multi-value, a pointer to memory is returned. The native code would then have to load values from memory into registers, introducing latency.
• With multi-value, if the number of return values is small and fits within the available CPU registers, the native function can simply place the results directly into registers, completely bypassing memory access for those values. This is a profound optimization, eliminating memory-related stalls and improving cache utilization.

4. Improved Foreign Function Interface (FFI) Performance and Clarity:

When WebAssembly modules interact with JavaScript (or other host environments), the Multi-Value proposal simplifies the interface. JavaScript's `WebAssembly.Instance.exports` now directly exposes functions capable of returning multiple values, often represented as arrays or specialized objects in JavaScript. This reduces the need for manual marshalling/unmarshalling of data between Wasm's linear memory and JavaScript values, leading to:

• Faster Interoperability: Less data copying and transformation between the host and Wasm.
• Cleaner APIs: Wasm functions can expose more natural and expressive interfaces to JavaScript, aligning better with how modern JavaScript functions return multiple pieces of data (e.g., array destructuring).

5. Better Semantic Alignment and Expressiveness:

The Multi-Value feature allows Wasm to better reflect the semantics of many source languages. This means less impedance mismatch between the high-level language concepts (like tuples, multiple return values) and their Wasm representation. This leads to:

• More Idiomatic Code: Compilers can generate Wasm that is a more direct translation of the source code, making debugging and understanding the compiled Wasm easier for advanced users.
• Increased Developer Productivity: Developers can write code in their preferred language without worrying about artificial Wasm limitations forcing them into awkward workarounds.

Practical Implications and Diverse Use Cases

The multi-value function call convention has a wide array of practical implications across various domains, making WebAssembly an even more powerful tool for global developers:

• Scientific Computing and Data Processing:
  • Mathematical functions returning (value, error_code) or (real_part, imaginary_part).
  • Vector operations returning (x, y, z) coordinates or (magnitude, direction).
  • Statistical analysis functions returning (mean, standard_deviation, variance).
• Image and Video Processing:
  • Functions extracting image dimensions returning (width, height).
  • Color conversion functions returning (red, green, blue, alpha) components.
  • Image manipulation operations returning (new_width, new_height, status_code).
• Cryptography and Security:
  • Key generation functions returning (public_key, private_key).
  • Encryption routines returning (cipher_text, initialization_vector) or (encrypted_data, authentication_tag).
  • Hashing algorithms returning (hash_value, salt).
• Game Development:
  • Physics engine functions returning (position_x, position_y, velocity_x, velocity_y).
  • Collision detection routines returning (hit_status, impact_point_x, impact_point_y).
  • Resource management functions returning (resource_id, status_code, remaining_capacity).
• Financial Applications:
  • Interest calculation returning (principal, interest_amount, total_payable).
  • Currency conversion returning (converted_amount, exchange_rate, fees).
  • Portfolio analysis functions returning (net_asset_value, total_returns, volatility).
• Parsers and Lexers:
  • Functions parsing a token from a string returning (token_value, remaining_string_slice).
  • Syntax analysis functions returning (AST_node, next_parse_position).
• Error Handling:
  • Any operation that can fail, returning (result, error_code) or (value, boolean_success_flag). This is a common pattern in Go and Rust, now efficiently translated to Wasm.

These examples illustrate how multi-value simplifies the interface of Wasm modules, making them more natural to write, more efficient to execute, and easier to integrate into complex systems. It removes a layer of abstraction and cost that previously hindered Wasm's adoption for certain types of computations.

Before Multi-Value: The Workarounds and Their Hidden Costs

To fully appreciate the optimization brought by multi-value, it's essential to understand the detailed costs of the previous workarounds. These aren't just minor inconveniences; they represent fundamental architectural compromises that affected performance and developer experience.

1. Heap Allocation (Tuples/Structs) Revisited:

When a Wasm function needed to return more than one scalar value, the common strategy involved:

1. The caller allocating a region in Wasm's linear memory to act as a "return buffer."
2. Passing a pointer to this buffer as an argument to the function.
3. The function writing its multiple results into this memory region.
4. The function returning a status code or a pointer to the now-populated buffer.

Alternatively, the function itself might allocate memory, populate it, and return a pointer to the newly allocated region. Both scenarios involve:

• `malloc`/`free` Overhead: Even in a simple Wasm runtime, `malloc` and `free` are not free operations. They require maintaining a list of free memory blocks, searching for suitable sizes, and updating pointers. This consumes CPU cycles.
• Cache Inefficiency: Heap-allocated memory can be fragmented across the physical memory, leading to poor cache locality. When the CPU accesses a value from the heap, it might incur a cache miss, forcing it to fetch data from slower main memory. Stack operations, by contrast, often benefit from excellent cache locality because the stack grows and shrinks predictably.
• Pointer Indirection: Accessing values via a pointer requires an extra memory read (first to get the pointer, then to get the value). While seemingly minor, this adds up in performance-critical code.
• Garbage Collection Pressure (in hosts with GC): If the Wasm module is integrated into a host environment with a garbage collector (like JavaScript), managing these heap-allocated objects can add pressure to the garbage collector, potentially leading to pauses.
• Code Complexity: Compilers needed to generate code for allocating, writing, and reading from memory, which is significantly more complex than simply pushing and popping values from a stack.

2. Global Variables:

Using global variables to return results has several severe limitations:

• Lack of Reentrancy: If a function that uses global variables for results is called recursively or concurrently (in a multi-threaded environment), its results will be overwritten, leading to incorrect behavior.
• Increased Coupling: Functions become tightly coupled through shared global state, making modules harder to test, debug, and refactor independently.
• Reduced Optimizations: Compilers often have a harder time optimizing code that relies heavily on global state because changes to globals can have far-reaching, non-local effects that are difficult to track.

3. Encoding into a Single Value:

While conceptually simple for very specific cases, this method falls apart for anything beyond trivial data packing:

• Limited Type Compatibility: Only works if multiple smaller values can fit exactly into a larger primitive type (e.g., two i16 into an i32).
• Bitwise Operations Cost: Packing and unpacking require bitwise shift and mask operations, which, while fast, add to the instruction count and complexity compared to direct stack manipulation.
• Maintainability: Such packed structures are less readable and more prone to errors if the encoding/decoding logic is not perfectly matched between caller and callee.

In essence, these workarounds forced compilers and developers to write code that was either slower due to memory overheads, or more complex and less robust due to state management issues. Multi-value directly addresses these fundamental problems, allowing Wasm to perform more efficiently and naturally.

The Technical Deep Dive: How Multi-Value is Implemented

The Multi-Value proposal introduced changes at the core of the WebAssembly specification, affecting its type system and instruction set. These changes enable the seamless handling of multiple values on the stack.

1. Type System Enhancements:

The WebAssembly specification now allows function types to declare multiple return values. A function signature is no longer limited to (params) -> (result) but can be (params) -> (result1, result2, ..., resultN). Similarly, input parameters can also be expressed as a sequence of types.

For example, a function type might be declared as [i32, i32] -> [i64, i32], meaning it takes two 32-bit integers as input and returns one 64-bit integer and one 32-bit integer.

2. Stack Manipulation:

The Wasm operand stack is designed to handle this. When a function with multiple return values completes, it pushes all its declared return values onto the stack in order. The calling function can then consume these values sequentially. For instance, a call instruction followed by a multi-value function will result in multiple items being present on the stack, ready for subsequent instructions to use.

            ;; Example Wasm pseudo-code for a multi-value function
(func (export "get_pair") (result i32 i32)
  (i32.const 10)  ;; Push first result
  (i32.const 20)  ;; Push second result
)

;; Caller Wasm pseudo-code
(call "get_pair") ;; Puts 10, then 20 on stack
(local.set $y)    ;; Pop 20 into local $y
(local.set $x)    ;; Pop 10 into local $x
;; Now $x = 10, $y = 20
            

              
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This direct stack manipulation is the core of the optimization. It avoids intermediate memory writes and reads, directly leveraging the speed of the CPU's stack operations.

3. Compiler and Tooling Support:

For multi-value to be truly effective, compilers targeting WebAssembly (like LLVM, Rustc, Go compiler, etc.) and Wasm runtimes must support it. Modern versions of these tools have embraced the multi-value proposal. This means that when you write a function in Rust returning a tuple (i32, i32) or in Go returning (int, error), the compiler can now generate Wasm bytecode that directly utilizes the multi-value call convention, resulting in the optimizations discussed.

This broad tooling support has made the feature seamlessly available to developers, often without them needing to explicitly configure anything beyond using up-to-date toolchains.

4. Host Environment Interaction:

Host environments, particularly web browsers, have updated their JavaScript APIs to correctly handle multi-value Wasm functions. When a JavaScript host calls a Wasm function that returns multiple values, these values are typically returned in a JavaScript array. For example:

            // JavaScript host code
const { instance } = await WebAssembly.instantiate(wasmBytes, {});
const results = instance.exports.get_pair(); // Assuming get_pair is a Wasm function returning (i32, i32)
console.log(results[0], results[1]); // e.g., 10 20
            

              
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This clean and direct integration further minimizes overhead at the host-Wasm boundary, contributing to overall performance and ease of use.

Real-World Performance Gains and Benchmarks (Illustrative Examples)

While precise global benchmarks depend heavily on specific hardware, Wasm runtime, and workload, we can illustrate the conceptual performance gains. Consider a scenario where a financial application performs millions of calculations, each requiring a function that returns both a calculated value and a status code (e.g., (amount, status_enum)).

Scenario 1: Pre-Multi-Value (Heap Allocation)

A C function compiled to Wasm might look like this:

            // C pseudo-code pre-multi-value
typedef struct { int amount; int status; } CalculationResult;
CalculationResult* calculate_financial_data(int input) {
    CalculationResult* result = (CalculationResult*)malloc(sizeof(CalculationResult));
    if (result) {
        result->amount = input * 2;
        result->status = 0; // Success
    } else {
        // Handle allocation failure
    }
    return result;
}

// Caller would call this, then access result->amount and result->status
// and critically, eventually call free(result)
            

              
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Each call to calculate_financial_data would involve:

• A call to malloc (or similar allocation primitive).
• Writing two integers to memory (potentially cache misses).
• Returning a pointer.
• The caller reading from memory (more cache misses).
• A call to free (or similar deallocation primitive).

If this function is called, for instance, 10 million times in a simulation, the cumulative cost of memory allocation, deallocation, and indirect memory access would be substantial, potentially adding hundreds of milliseconds or even seconds to the execution time, depending on the memory allocator's efficiency and CPU architecture.

Scenario 2: With Multi-Value

A Rust function compiled to Wasm, leveraging multi-value, would be much cleaner:

            // Rust pseudo-code with multi-value (Rust tuples compile to multi-value Wasm)
#[no_mangle]
pub extern "C" fn calculate_financial_data(input: i32) -> (i32, i32) {
    let amount = input * 2;
    let status = 0; // Success
    (amount, status)
}

// Caller would call this and directly receive (amount, status) on the Wasm stack.
            

              
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Each call to calculate_financial_data now involves:

• Pushing two integers onto the Wasm operand stack.
• The caller directly popping these two integers from the stack.

The difference is profound: the memory allocation and deallocation overhead is completely eliminated. The direct stack manipulation leverages the fastest parts of the CPU (registers and L1 cache) as the Wasm runtime translates stack operations directly to native register/stack operations. This can lead to:

• CPU Cycle Reduction: Significant reduction in the number of CPU cycles per function call.
• Memory Bandwidth Savings: Less data moved to/from main memory.
• Improved Latency: Faster completion of individual function calls.

In highly optimized scenarios, these performance gains can be in the range of 10-30% or even more for code paths that frequently call functions returning multiple values, depending on the relative cost of memory allocation on the target system. For tasks like scientific simulations, data processing, or financial modeling, where millions of such operations occur, the cumulative impact of multi-value is a game-changer.

Best Practices and Considerations for Global Developers

While multi-value offers significant advantages, its judicious use is key to maximizing benefits. Global developers should consider these best practices:

When to Use Multi-Value:

• Natural Return Types: Use multi-value when your source language naturally returns multiple logically related values (e.g., tuples, error codes, coordinates).
• Performance-Critical Functions: For functions called frequently, especially in inner loops, multi-value can yield substantial performance improvements by eliminating memory overhead.
• Small, Primitive Return Values: It's most effective for a small number of primitive types (i32, i64, f32, f64). The number of values that can be efficiently returned in CPU registers is limited.
• Clear Interface: Multi-value makes function signatures clearer and more expressive, which improves code readability and maintainability for international teams.

When Not to Rely Solely on Multi-Value:

• Large Data Structures: For returning large or complex data structures (e.g., arrays, large structs, strings), it's still more appropriate to allocate them in Wasm's linear memory and return a single pointer. Multi-value is not a substitute for proper memory management of complex objects.
• Infrequently Called Functions: If a function is called rarely, the overhead of previous workarounds might be negligible, and the optimization from multi-value less impactful.
• Excessive Number of Return Values: While the Wasm spec technically allows many return values, practically, returning a very large number of values (e.g., dozens) might saturate the CPU's registers and still lead to values spilling onto the stack in native code, diminishing some of the register-based optimization benefits. Keep it concise.

Impact on Debugging:

With multi-value, the Wasm stack state might appear slightly different than pre-multi-value. Debugger tooling has evolved to handle this, but understanding the stack's direct manipulation of multiple values can be helpful when inspecting Wasm execution. Source map generation from compilers typically abstracts this away, allowing debugging at the source language level.

Toolchain Compatibility:

Always ensure your Wasm compiler, linker, and runtime are up-to-date to fully leverage multi-value and other modern Wasm features. Most modern toolchains automatically enable this. For example, Rust's wasm32-unknown-unknown target, when compiled with recent Rust versions, will automatically use multi-value when returning tuples.

The Future of WebAssembly and Multi-Value

The Multi-Value proposal is not an isolated feature; it's a foundational component that paves the way for even more advanced WebAssembly capabilities. Its elegant solution to a common programming problem strengthens Wasm's position as a robust, high-performance runtime for a diverse range of applications.

• Integration with Wasm GC: As the WebAssembly Garbage Collection (Wasm GC) proposal matures, allowing Wasm modules to directly allocate and manage garbage-collected objects, multi-value will seamlessly integrate with functions returning references to these managed objects.
• The Component Model: The WebAssembly Component Model, designed for interoperability and module composition across languages and environments, heavily relies on robust and efficient parameter passing. Multi-value is a crucial enabler for defining clear, high-performance interfaces between components without marshaling overheads. This is particularly relevant for global teams building distributed systems, microservices, and pluggable architectures.
• Broader Adoption: Beyond web browsers, Wasm runtimes are seeing increased adoption in server-side applications (Wasm on the server), edge computing, blockchain, and even embedded systems. The performance benefits of multi-value will accelerate Wasm's viability in these resource-constrained or performance-sensitive environments.
• Ecosystem Growth: As more languages compile to Wasm and more libraries are built, multi-value will become a standard and expected feature, allowing for more idiomatic and efficient code across the entire Wasm ecosystem.

Conclusion

The WebAssembly Multi-Value Function Call Convention represents a significant leap forward in Wasm's journey towards becoming a truly universal and high-performance computation platform. By directly addressing the inefficiencies of single-value returns, it unlocks substantial parameter passing optimizations, leading to faster execution, reduced memory overhead, and simpler code generation for compilers.

For developers worldwide, this means being able to write more expressive, idiomatic code in their preferred languages, confident that it will compile to highly optimized WebAssembly. Whether you are building complex scientific simulations, responsive web applications, secure cryptographic modules, or performant serverless functions, leveraging multi-value will be a key factor in achieving peak performance and enhancing developer experience. Embrace this powerful feature to build the next generation of efficient and portable applications with WebAssembly.

Explore further: Dive into the WebAssembly specification, experiment with modern Wasm toolchains, and witness the power of multi-value in your own projects. The future of high-performance, portable code is here.