C++: `std::valarray`

Grok summary

Disclaimer: Grok generated document.

std::valarray is a C++ standard library container introduced in C++98 specifically for high-performance numerical computations on arrays of numeric types (like float, double, int, etc.). It was designed with the intention of allowing aggressive optimizations for mathematical operations, especially on entire arrays at once.

Main Intended Use Cases

Element-wise mathematical operations with a clean syntax

std::valarray<double> a = {1, 2, 3, 4};
std::valarray<double> b = {5, 6, 7, 8};

auto c = a + b;        // {6, 8, 10, 12}
auto d = a * b;        // {5, 12, 21, 32}
auto e = sin(a) + cos(b);  // applies sin/cos element-wise

Built-in support for mathematical functions (applied element-wise):
- abs(), sqrt(), exp(), log(), sin(), cos(), tan(), acos(), asin(), atan(), sinh(), cosh(), tanh(), pow(), etc.

Slicing and indirect array access (advanced indexing)

std::valarray<double> v(12);
std::slice s(0, 4, 3);           // start=0, length=4, stride=3
v[s] = 99.0;                     // v[0], v[3], v[6], v[9] = 99.0
auto subset = v[std::gslice(...)];  // multi-dimensional-like slicing

Operations with scalars

auto x = a * 2.0 + 5.0;   // every element multiplied by 2 and add 5

Why it was created

In the 1990s, people wanted something like MATLAB/NumPy in C++:

Vectorized operations without explicit loops
Potential for compiler optimizations (expression templates, loop fusion, SIMD)
Clean mathematical syntax

Reality Check: Why `std::valarray` is rarely used today

Despite good intentions, std::valarray has major drawbacks and is widely considered a failed experiment:

Issue	Explanation
Poor performance in practice	Most compilers do not apply the expected aggressive optimizations. Often slower than hand-written loops or `std::vector` with SIMD.
Expression templates missing	The original design assumed expression templates (like in Blitz++ or Eigen), but the standard version uses temporary objects → many copies.
Awkward, non-intuitive API	Slicing (`std::slice`, `std::gslice`, `std::mask_array`, `std::indirect_array`) is complex and hard to use correctly.
No dynamic allocation control	Always copies on operations unless compiler optimizes heavily (which it usually doesn’t).
No iterator support pre-C++11	Made it incompatible with STL algorithms.
Very little real-world adoption	Almost no major numerical library (Eigen, Armadillo, Blaze, etc.) uses or recommends it.

Modern Alternatives (2025)

Library	Advantages over `std::valarray`
Eigen	Extremely fast, expression templates, lazy evaluation, excellent SIMD, MATLAB-like syntax
blaze-lib	Modern C++ design, hybrid CPU/GPU, great performance
Armadillo	MATLAB-like syntax, integrates with LAPACK/BLAS
xtensor	NumPy-like API, multi-dimensional, lazy evaluation
std::mdspan (C++23) + std::simd (upcoming)	Future standard solution for multi-dimensional arrays and explicit SIMD
Simple `std::vector` + loops + SIMD intrinsics/OpenMP	Often faster and much clearer

Verdict

Use std::valarray only if:

You are maintaining very old numerical code that already uses it.
You are doing educational experiments with the original C++98 numerical array design.

Do NOT use std::valarray for new code if performance or clarity matters.

For any serious numerical work in modern C++, use Eigen, blaze, xtensor, or wait for the emerging standard solutions based on std::mdspan and std::simd.

In short: std::valarray is an interesting historical artifact with a great idea that never quite worked out in practice.

ChatGPT long explanation

Disclaimer: ChatGPT generated document.

Short version first: std::valarray is a numeric array type from <valarray> designed for element-wise math with strong optimization guarantees, not a general container like std::vector. It comes with slices, masks, and a bunch of math functions that operate on whole arrays at once.

Because you asked for “everything,” I’ll go through:

What std::valarray is and its design goals
Basic usage and core operations
Slicing, masking, and indirect access
The ecosystem: math functions, helper types, begin/end
Performance model, aliasing rules, expression templates
Comparison with std::vector and when to avoid/choose it
Modern C++ caveats and real-world advice

I’ll assume at least intermediate C++ and that you’re comfortable with templates.

1. What is `std::valarray`?

Definition:

#include <valarray>

template<class T>
class std::valarray;

Conceptually:

A numeric array intended for element-wise arithmetic and math.
Supports vectorized operations like a + b, a * 2.0, sqrt(a), etc.
Designed to allow the implementation to apply aggressive optimizations, including:
- No surprising aliasing between certain views
- Potential SIMD/vectorization
- Lazy expression evaluation via expression templates (implementation-dependent) (en.cppreference.com)

In contrast to std::vector:

valarray is not a general-purpose container (no standard iterators before C++11, no allocator, etc.).
The standard explicitly gives implementations room to optimize it differently (even to the point of using a replacement type for math functions).

2. Basic usage & core operations

Construction

Typical constructors:

std::valarray<int> a;                  // default, size 0
std::valarray<double> b(10);           // size 10, value-initialized
std::valarray<double> c(3.14, 8);      // size 8, initialized to 3.14
int raw[] = {1,2,3,4};
std::valarray<int> d(raw, 4);          // from C array

std::valarray<int> e = {1, 2, 3, 4};   // initializer_list (C++11)

Copy/move semantics are straightforward (typical value semantics).

Deduction guides exist from C++17, so std::valarray v{1,2,3} deduces int. (en.cppreference.com)

Element access

std::valarray<double> v(10);
v[0] = 1.0;
double x = v[3];

operator[] does no bounds checking (like vector::operator[]).
There is no at() member.

Size & resizing

std::size_t n = v.size();  // number of elements

v.resize(20);              // size changes, values implementation-defined
                           // can invalidate begin()/end() iterators

resize may or may not preserve values; the standard leaves behavior flexible to preserve optimization freedom (check your implementation docs if you care). (en.cppreference.com)

Assignment & broadcasting

std::valarray<double> a(10), b(10);
a = 1.0;        // all elements become 1.0 (broadcast scalar)
b = a;          // element-wise copy
b += 2.0;       // add 2.0 to each element

Scalar assignment and scalar ops are broadcast over all elements.

3. Element-wise operators

valarray supports a large set of operators, both member and non-member, working element-wise:

Arithmetic: +, -, *, /, %
Bitwise: &, |, ^, <<, >>
Logical: &&, ||, unary !
Relational: ==, !=, <, >, <=, >=

These can work in combinations:

std::valarray<double> a = {1.0, 2.0, 3.0};
std::valarray<double> b = {10.0, 20.0, 30.0};

auto c = a + b;     // {11, 22, 33}
auto d = 2.0 * a;   // {2, 4, 6}
auto e = a * a + b; // {11, 24, 39}

You also get compound assignments:

a += b;
a *= 2.0;

Relational/logical ops return std::valarray<bool> (or implementor’s replacement type), which is crucial for masks.

4. Math functions on whole valarrays

<valarray> provides overloads of many math functions that operate element-wise on a valarray. (en.cppreference.com)

Unary math

For std::valarray<T>:

Elementary: abs, exp, log, log10, sqrt
Trig: sin, cos, tan, asin, acos, atan
Hyperbolic: sinh, cosh, tanh

Example:

std::valarray<double> a = {0.0, 1.0, 2.0};
std::valarray<double> b = std::sin(a);   // apply sin to each element
std::valarray<double> c = std::sqrt(a);  // sqrt element-wise

Binary math

pow and atan2 have overloads:

std::valarray<double> base = {1.0, 2.0, 3.0};
std::valarray<double> exponents = {2.0, 2.0, 2.0};

auto squares = std::pow(base, exponents);   // element-wise pow
auto cube    = std::pow(base, 3.0);         // pow against scalar

Key detail: These functions are allowed to return an implementation-defined replacement type with valarray-compatible API, to enable expression templates and optimizations. (en.cppreference.com)

5. Aggregation & simple algorithms

valarray has built-in aggregations:

std::valarray<double> a = {1.0, 2.0, 3.0, 4.0};

double s = a.sum();    // 10
double p = a.prod();   // 24
double mn = a.min();   // 1
double mx = a.max();   // 4

// shift & cshift
auto left  = a.shift(1);   // [2,3,4,0] (implementation-defined fill)
auto right = a.cshift(1);  // circular shift: [4,1,2,3]

// apply arbitrary unary function
auto squares = a.apply([](double x){ return x * x; }); // {1,4,9,16}

apply returns a (possibly replacement) valarray-like type. (mooshak.dcc.fc.up.pt)

6. Slices, masks, and indirect access

This is where valarray gets more interesting, and also more exotic.

Helper types (all in <valarray>): (en.cppreference.com)

std::slice / std::slice_array<T>
std::gslice / std::gslice_array<T>
std::mask_array<T>
std::indirect_array<T>

These are “views” that let you select and modify subsets of a valarray.

6.1 `slice` & `slice_array` – 1D strided views

std::slice describes a start, size, stride:

std::slice s(start, size, stride);

Given a valarray v, v[s] returns a slice_array<T> view on those elements.

Example: pick every second element, starting at 0:

std::valarray<int> v = {0,1,2,3,4,5,6,7,8,9};
std::slice evens(0, v.size()/2 + v.size()%2, 2); // start=0, #elements=5, stride=2

std::valarray<int> ev = v[evens];  // {0,2,4,6,8}
v[evens] = 42;                     // write-back: v becomes {42,1,42,3,42,5,42,7,42,9}

slice_array<T> objects are proxy types:

They don’t own memory.
Assigning to them writes back to the underlying valarray.
They’re mainly used on the LHS of assignments.

6.2 `gslice` & `gslice_array` – “multi-dimensional” general slices

std::gslice (general slice) extends this to N-dimensional indexing on a flat valarray:

Constructed with:
- start index
- array of lengths (per dimension)
- array of strides (per dimension)

std::valarray<int> v(100); // think of it as 10x10 matrix

std::size_t start = 0;
std::valarray<std::size_t> lengths = {10, 10};  // 2D: 10x10
std::valarray<std::size_t> strides = {10, 1};   // row-major

std::gslice g(start, lengths, strides);

// Get the diagonal:
std::valarray<std::size_t> diag_lengths = {10};
std::valarray<std::size_t> diag_strides = {11}; // stride between diagonal elements
std::gslice diag(0, diag_lengths, diag_strides);

auto diag_view = v[diag];      // gslice_array<int>
diag_view = 1;                 // sets all diagonal elements to 1

gslice_array<T> is again a proxy type like slice_array<T>.

6.3 `mask_array` – element selection via boolean mask

You can filter a valarray by a std::valarray<bool> mask:

std::valarray<double> data = {1, -2, 3, -4, 5};
std::valarray<bool> mask = (data > 0);   // {true, false, true, false, true}

std::valarray<double> positives = data[mask];  // {1, 3, 5}
data[mask] *= 10;                             // only positive elements * 10

data[mask] returns a mask_array<double> view.
Assignment applies only to the elements where mask is true.

6.4 `indirect_array` – indexing by indices

You can select arbitrary elements by an index array:

std::valarray<int> data = {10, 20, 30, 40, 50};
std::valarray<std::size_t> idx = {4, 0, 3};

std::valarray<int> picked = data[idx];  // {50, 10, 40}
data[idx] += 1;                         // data becomes {11,20,30,41,51}

data[idx] is an indirect_array<int> view.

7. Iteration and `std::begin` / `std::end`

Originally, valarray did not expose iterators; this was intentional, to keep optimizations free of aliasing concerns.

Starting C++11, you have std::begin / std::end overloads for valarray: (en.cppreference.com)

std::valarray<int> v = {1, 2, 3};
for (int& x : v) {
    x *= 2;
}

// or algorithms
std::for_each(std::begin(v), std::end(v),
              [](int& x){ x += 1; });

Important details:

The iterator type is implementation-defined (not T* necessarily).
begin(v) / end(v) iterators are invalidated by v.resize().
They are intended primarily so that range-for loops and algorithms can work.

Also: std::begin / std::end are not guaranteed to accept replacement types returned by expression templates (v1 + v2). So you should usually do:

auto tmp = std::valarray<int>(v1 + v2);
std::begin(tmp);  // OK

Not:

std::begin(v1 + v2);  // not portable

8. Expression templates & replacement types

The standard deliberately gives implementations a lot of freedom:

The math functions (sin, cos, exp, log, etc.) and apply may return a type that is not literally std::valarray<T> but behaves like one. (en.cppreference.com)
This allows implementations to:
- Use expression templates to delay evaluation.
- Fuse multiple operations into a single pass (e.g., compute a + b + c in one loop).
- Optimize memory usage.

Constraints on such replacement types:

Must provide all const valarray members.
Must allow construction of valarray, slice_array, gslice_array, mask_array, indirect_array from it.
Any f(const valarray<T>&) (except begin/end) must also accept replacement types.
Similar for f(const valarray<T>&, const valarray<T>&) accepting replacement combos.
Can’t add more than two extra template nesting levels compared to its arguments.

Practically: for you as a user, this means:

You can usually treat them like valarray.
But you sometimes need an explicit std::valarray<T> temporary when interfacing with things like iterators or APIs expecting a genuine valarray.

9. Performance model & aliasing guarantees

std::valarray and its helper types are specified to be free of certain forms of aliasing, enabling optimizations similar to restrict in C. (en.cppreference.com)

In broad strokes:

The implementation may assume a[i], b[j] don’t unexpectedly alias unless they come from overlapping views of the same underlying valarray in ways allowed by the standard.
The slicing/masking abstractions are designed so that the implementation knows the structure of accesses and can vectorize easily.

This was one of the original motivations: provide something the compiler can reliably optimize for numeric code.

However:

In modern C++, compilers often optimize std::vector very well too.
The actual performance advantage of valarray is implementation-dependent and in practice often negligible or worse than vector, depending on the library.

10. Comparison with `std::vector` & why it’s rarely used today

Advantages vs `std::vector`

Element-wise operations built in: v1 + v2, sqrt(v), sin(v) etc., without writing loops.
Rich slicing/masking API out of the box (slices, masks, indirect).
Optimization license: the standard allows the implementation to be clever.

Disadvantages vs `std::vector`

Less idiomatic in modern C++:
- Most numeric & scientific C++ ecosystems either:
  - Use std::vector + algorithms, or
  - Use dedicated libraries (Eigen, Blaze, xtensor, Armadillo, etc.).
Weird semantics:
- resize behavior not clearly defined for preserving elements.
- Expression-template replacement types can surprise you (type not actually std::valarray).
Weaker container interface:
- No push_back, no iterator member types, no reserve, no allocator parameter, etc.
- Not part of generic container algorithms culture.
Limited ecosystem:
- Most third-party libraries don’t take std::valarray parameters.

Because of this, many experts recommend:

Use std::vector as your standard dynamic array.
Use specialized math/numeric libraries if you want vectorized operations and slices.
Use valarray only in niche situations or legacy code.

11. A quick worked example: simple signal processing

Just to show the intended style:

#include <valarray>
#include <cmath>

using std::valarray;

int main() {
    const std::size_t N = 1024;
    valarray<double> t(N);
    valarray<double> signal(N);

    const double dt = 1.0 / 44100.0;
    const double freq = 440.0;

    // Fill time array
    for (std::size_t i = 0; i < N; ++i) {
        t[i] = i * dt;
    }

    // Sine wave
    signal = std::sin(2.0 * M_PI * freq * t);

    // Apply a simple gain, then offset, then square
    auto processed = std::pow(2.0 * signal + 0.1, 2.0);

    // Compute some stats
    double mean = processed.sum() / processed.size();
    double maxv = processed.max();
    double minv = processed.min();

    (void)mean; (void)maxv; (void)minv;
}

This is exactly the use-case valarray was designed for: concise numeric transformations.

12. Modern C++ & `std::valarray`: practical advice

Given your background and interest in high-quality C++:

When might you reasonably use `std::valarray`?

Small standalone numeric tools where you:
- Want quick element-wise math with minimal code.
- Don’t need interoperability with other libraries.
Educational code/examples illustrating vectorized operations without bringing in Eigen/Blaze.

When to avoid it

In public APIs: std::vector<T> or std::span<T> are better choices for interoperability.
In performance-critical numeric code where you’re already using a heavy-duty numeric library.
Whenever you need fine-grained control over allocation/growth behavior.

Idiomatic replacement in modern style

Use std::vector<T> + ranges/algorithms (std::transform, etc.)
Wrap domain-specific operations in small functions or types.
For slicing/masking: consider libraries (Eigen, Blaze, xtensor) that support this cleanly.

13. Summary

Key points to keep in your mental model:

std::valarray<T> = numeric array designed for element-wise operations and optimizations.
Comes with:
- Element-wise operators and math functions (sin, cos, sqrt, etc.).
- Aggregations (sum, min, max, prod).
- Slices (slice, gslice), masks (mask_array), and indirect views (indirect_array).
Has unusual optimization latitude: math functions & apply may return valarray-like replacement types to enable expression templates.
Not a general-purpose container; less idiomatic in modern C++ than std::vector.
Often replaced in practice by std::vector + numeric libraries.

If you’d like next, I can:

Show side-by-side valarray vs vector + algorithms versions of some numeric tasks, or
Go deep into how you’d implement a tiny valarray-like expression-template engine yourself (which dovetails nicely with your C++20/expression-template interests).

Is std::valarray making a comeback in C++26? Are there new features added to it and how is it relevant?

Short answer: no, std::valarray is not making some big heroic comeback in C++26. It’s getting a bit of cleanup and will likely remain what it already is: a quirky numeric container that’s kept alive and made consistent, but not turned into a modern flagship.

I’ll split it into:

What’s actually changing around valarray for C++26
Other proposals that merely touch valarray
Whether this signals a “comeback”
How relevant valarray is in modern C++ compared to the new toys

1. The one real C++26-facing paper: P3016 (begin/end/data/empty)

The only substantial C++26 work that directly targets std::valarray is Arthur O’Dwyer’s paper:

P3016 – “Resolve inconsistencies in begin/end for valarray and braced initializer lists” (open-std.org)

Roughly speaking, it does for valarray and initializer lists what you’d expect from a modern container / range:

Cleans up how the free functions std::begin, std::end, std::data, and std::empty behave when applied to std::valarray.
Resolves a bunch of Library Working Group issues around those functions and to braced initializer lists. (open-std.org)
Makes the wording more consistent so generic code using ranges and these helpers behaves predictably with valarray.

The design in P3016 has been approved for C++26 by the Library Evolution Working Group (LEWG) and has been moving through Library Working Group (LWG) as part of the C++26 pipeline. (open-std.org)

What this means in practice:

Range-based for and generic algorithms already work with valarray today via the std::begin/std::end overloads, but P3016 tightens and simplifies the rules.
It helps avoid weird corner cases with expression-template replacement types, temporaries, and initializer lists (like some of the classic “invalid range expression” surprises). (Stack Overflow)
It does not add new algorithms, slicing features, math functions, or ranges integration; it’s maintenance/consistency work.

So: yes, valarray is being touched for C++26, but in a housekeeping sense.

2. Other proposals that mention `valarray`

There are a couple of places where valarray gets mentioned in other C++26-era papers:

2.1 Implication operator (`=>`) and `valarray`

Walter Brown’s proposal P2971 – “Implication for C++” introduces a new logical operator => for the language. In its library impact section, it notes that std::valarray should be updated to accommodate the new operator, similar to how it already supports && and || element-wise. (wg21.link)

If => is adopted into the language and that wording is kept, you’d see:

A new element-wise operator=> for std::valarray<bool> (and maybe more, depending on wording).
Symmetry with logical_and / logical_or and other logical facilities in <functional>.

Again, that’s tiny—it’s just one more operator in the list, not a redesign or expansion of valarray.

2.2 Being referenced in modern docs / reflection examples

Modern proposals like reflection (P2996 “Reflection for C++26”) and other library papers sometimes list <valarray> alongside other headers simply because it is part of the standard library and must be accounted for in wording and examples. (open-std.org)

That’s about correctness and coverage, not new features.

3. Is this a “comeback”?

Realistically: no.

If you look at:

The official C++26 trip reports and blog posts (Herb Sutter’s, committee trip reports, etc.), the big headline items are contracts, reflection, linear algebra (BLAS-style algorithms), std::simd, ranges work, library hardening, etc.—valarray is never a star of the show. (LinkedIn)
The WG21 paper lists for 2024–2025: valarray only appears explicitly in P3016, plus that small note in P2971. There’s no “valarray 2.0” or “ranges-aware valarray” or anything like that. (open-std.org)

Community sentiment also hasn’t changed much:

People still describe valarray as “not really maintained and updated with more features” and point out that most numeric work is done with std::vector + libraries like Eigen/Blaze/xTensor instead. (Computational Science Stack Exchange)
It remains niche despite occasional YouTube episodes or articles rediscovering it.

So, C++26 is tidying it up, not rejuvenating it.

4. How relevant is `std::valarray` in the C++26 ecosystem?

I’d frame its relevance in three layers:

4.1 Still standard, still supported, not deprecated

std::valarray is fully specified in the latest standard drafts and reference material; there is no deprecation or removal proposal on the table. (en.cppreference.com)
It continues to offer:
- Element-wise operators and math functions,
- Slices, masks, indirect access,
- Some aliasing guarantees that can, in principle, enable optimizations. (en.cppreference.com)

So if you use it, you’re not using some zombie facility that’s about to be yanked.

4.2 Competing with the new numeric tools

But compare that to where C++26 is actually investing:

Linear algebra: the BLAS-style algorithms proposal for C++26 gives you standardized linear algebra operations that are meant to be efficient and interoperable with HPC backends. (LinkedIn)
std::simd: vectorization support already merged into the draft gives you explicit, portable SIMD programming for numeric workloads. (Sutter’s Mill)
std::mdspan, ranges, etc., offer modern ways of handling multi-dimensional data and composition of algorithms. (en.cppreference.com)

In that landscape, valarray is:

An older, limited attempt at “vectorized numeric container” with a very specific design philosophy,
Lacking integration with ranges, mdspan, linear algebra algorithms, etc.,
And generally overshadowed by vector + modern libraries and tools.

4.3 Where it still can matter

There are still some realistic niches:

Quick-and-dirty numeric experiments where you want element-wise operations and slices without pulling in a heavy external library.
Legacy or constrained environments where you don’t want dependency bloat but have compilers that optimize valarray reasonably well (Intel’s docs still talk about their optimized valarray implementation). (Intel)
Didactic code showing array math without introducing a full-blown linear algebra library.

But for modern, serious numerical code in the C++26 era, the direction is clearly:

std::vector / std::span / std::mdspan for storage and views,
std::simd and standardized linear algebra / numerics for performance,
Specialized libraries for anything beyond that.

TL;DR

Is std::valarray making a comeback in C++26? No. It’s being cleaned up (P3016) and may gain a tiny tweak for a new logical operator, but there’s no large new investment.
Are there new features? Only minor ones:
- Clearer, more consistent behavior for std::begin/std::end/std::data/std::empty when used with valarray (P3016). (open-std.org)
- Potential support for the proposed implication operator => in element-wise operations if that operator is adopted (P2971). (wg21.link)
How is it relevant? It remains a supported, standard numeric container with element-wise math and slicing, but it’s mostly in maintenance mode. In C++26, the real action for numerics is around linear algebra, std::simd, ranges, and mdspan; valarray is unlikely to be your primary tool unless you deliberately choose it for its specific style.

✔ Why Intel Parallel Studio is mentioned with `std::valarray`

1. Intel provided special vectorized implementations

Intel’s C++ standard library implementation (part of ICC and Parallel Studio) had:

• expression-template–like optimizations

std::valarray was designed so compilers could “peek inside” operations like:

valarray<double> a, b, c;
c = a + b * 2.0;

and internally generate fused, vectorized loops without temporaries.

Intel’s implementation actually did this through:

Loop fusion
Auto-vectorization
Removal of intermediate objects
Alignment-aware loads/stores

This was not true of GCC or Clang’s libstdc++, where `valarray` was basically just a wrapper on a dynamic array.

So with Intel's library, valarray could outperform:

std::vector + manual loops
naive numeric code
some BLAS-level operations

…in simple element-wise operations.

✔ 2. Intel marketed `valarray` as a high-performance numeric type

Intel documentation used to include statements like:

“Intel’s implementation of std::valarray is optimized for vectorization and can outperform conventional loop-based code.”

They would often show that:

c = a + b * d;

could auto-vectorize better than:

for (size_t i = 0; i < n; ++i)
    c[i] = a[i] + b[i] * d[i];

This sometimes gave significant speedups.

✔ 3. Intel Parallel Studio provided compiler pragmas and reports

Intel tools supported:

#pragma ivdep
#pragma simd
vectorization reports

These could confirm that valarray expressions generated single, vectorized loops with no aliasing concerns.

This worked hand-in-hand with the whole philosophy of valarray:

no aliasing
predictable layout
safe for vectorization

Intel leaned directly into that design.

✔ 4. Why this mattered in the past (and why it’s mentioned today)

valarray was created in the 90s with an explicit goal:

Enable compilers to auto-vectorize numeric code efficiently without aliasing penalties.

Intel was the only major vendor who fully embraced the idea and optimized for it.

Because of this, many people who worked in HPC, finance, or scientific computing with Intel Parallel Studio still associate:

valarray = "fast, vectorized, Intel-optimized numeric container"

✔ 5. Is this still relevant today?

Mostly no, and here’s why:

Intel Parallel Studio XE → obsolete

It has been replaced by the oneAPI toolchain (DPC++, ICX, etc.). The specialized valarray optimizations are not emphasized anymore.

Modern C++ has better tools

C++26/23 offers:

std::simd (actually portable SIMD!)
std::mdspan
standard BLAS-like linear algebra
strong optimizers in Clang/GCC/MSVC

std::simd is basically what valarray wanted to be — but realized correctly.

GCC and Clang never optimized `valarray` heavily

So the “Intel magic” wasn’t portable.

✔ TL;DR — Why people bring up Intel Parallel Studio

Because Intel compilers historically gave std::valarray special treatment, making it:

faster than std::vector in some numeric loops
implicitly vectorized
optimized using expression fusion
appealing for simple numerical kernels

Most of those advantages no longer matter in the modern ecosystem:

Intel's toolchain has moved on
C++ got std::simd
Numerics are shifting toward mdspan, ranges, linear algebra

But old HPC folks still remember that valarray was the thing Intel pushed for auto-vectorized math, so the association persists.

MangaD/cpp_valarray.md

C++: std::valarray