2026-07-18

GPU & Accelerated Computing:

• Inside TPU and GPU Clusters: The Anatomy of Collective Communication | Aleksa Gordić
• Porting High-Performance HIP Kernels to FlyDSL | AMD ROCm Blogs
• Understanding ATen: PyTorch’s tensor library | Red Hat Developer
• MatX - GPU-Accelerated Numerical Computing in Modern C++ | NVIDIA
• Metal Compute Shaders And C++ (PDF)

Linear Algebra:

• Show HN: LAPACK without Fortran77; a C11 translation (github.com/ilayn)
• semicolon-lapack

AI Compute Extensions (ACE):

• Intel Posts Initial GCC Compiler Patches For AI Compute Extensions “ACE” | Phoronix
• The AI Compute Extensions (ACE) for x86 (PDF, 147 KB)

Finite Elements & CFD:

• Templated FEM kernels: Benefits and Drawbacks
• From Particles on a Grid to the Wind Tunnel’s Digital Rival: The Rise of LBM in Defense Aviation | Dassault Systèmes SIMULIA

Programming:

• Story-time: C++, bounds checking, performance, and compilers | Chandler Carruth
• The Second Coming of the Command Line

Life Advice:

• Life Lessons from the First Half-Century of My Career | Communications of the ACM
• What the Middle Ages can teach us about preventing burnout | BBC Culture

2026-05-28

• Architecture & Systems are Changing: The Architect’s Role in the Era of Agentic Co-Design | Computer Architecture Today
• What’s new in Arm Performance Libraries 26.01 | Arm Developer Community
• perf-libs-sparse: Introducing a new open-source project for sparse linear algebra on Arm | Arm Developer Community
• NVIDIA Vera CPU Benchmarks: Olympus Cores Delivering The Best Performance Ever Seen On ARM | Phoronix
• Code Coverage Analysis for Fortran | Science and Technology Facilities Council Rutherford Appleton Laboratory (PDF)
• Why Japanese companies do so many different things | David Oks
• Advancing Computational Science with High-Order Finite Elements | Supercomputing Spotlights
• Human Bottlenecks | Fernando Borretti
• Strangely, Matrix Multiplications on GPUs Run Faster When Given “Predictable” Data! | Thonk From First Principles
• NVIDIA Took 20 Years to Ship 25 Lines of Python | Delanoe Pirard, Medium
• Can the Tile Paradigm Reshape the Competitive Landscape of the GPU Programming Ecosystem? | HyperAI

2026-05-22

• Alan Hindmarsh
• ODEPACK: A systematized collection
• ODE Meeting Argonne

2026-05-03

• Let Natural Selection Sharpen Your Writing | American Physical Society
• Writing is thinking | nature reviews bioengineering

2026-04-25

Object-oriented Design Principles:

• Why getter and setter methods are evil
• Getters/Setters. Evil. Period.

Misc:

• C Traps and Pitfalls (PDF, 117 KB)
• New features in OpenMP 5.0 and 5.1
• New features in OpenMP 5.1 and OpenMP 5.2
• Driving a New Era of Accelerated Computing Intel® Fortran Compiler (IFX)

2026-04-21

Intel:

• End Parallel Runtime Scheduling Conflicts with Thread Composability Manager

Misc:

• The Origins of GPU Computing | Communications of the ACM

2026-04-19

ArmPL:

• Arm Performance Libraries 25.04 and Arm Toolchain for Linux 20.1 Release
• Arm Performance Libraries 24.10

Batched Linear Algebra:

• The Design and Performance of Batched BLAS on Modern High-Performance Computing Systems (PDF)
• Batched BLAS APIs and Memory Layouts (PDF)
• batmat: Fast linear algebra routines for batches of small matrices
• Numerical Limitations for Compact BLAS and Compact LAPACK Routines
• Designing vector-friendly compact BLAS and LAPACK kernels
• INTEL® MKL Vectorized Compact routines (PDF)
• Adventures in Batched Linear Algebra in Intel® Math Kernel Library (PDF)
• Batched BLAS, SC24 BoF Session
• Introducing interleave-batched linear algebra functions in Arm PL
• BLASFEO - BLAS For Embedded Optimization

cuTile:

• Focus on Your Algorithm—NVIDIA CUDA Tile Handles the Hardware
• cuTile Python | Nvidia
• cuTile, the New/Old Kid on the Block: Python Programming Models for GPUs
• CUDA Tile IR
• cuTile Kernels
• cuTile.jl
• cuTile.jl: Bringing NVIDIA’s Tile-Based GPU Programming to Julia
• TileGym
• NVIDIA TileIR Internals: from CuTile to MLIR/LLVM to SASS
• CuTile on Blackwell: NVIDIA’s Compiler Moat Is Already Built
• CUDA Tile IR: Lessons from a Tile-Centric CUDA Dialect for MLIR

Interpreters:

• What opcode dispatch strategies are used in efficient interpreters?
• What are some common ways to optimise an interpreter?
• How not to make a virtual machine (label-based threading)
• Understanding Virtual Machine Dispatch through Duality
• Interpreters and virtual machines
• My experience crafting an interpreter with Rust
• Threaded Code
• Why Are My Bytecode Interpreters Slow? Hunting Truffles with VTune
• How to speed up dynamic dispatch by 20% using computed gotos in standard C++
• Computed goto for efficient dispatch tables
• Labels as Values
• Are Jump Tables Always Fastest

Unreachable:

• Portable assumptions
• Function to mark unreachable code
• Why do some languages not have an ‘unreachable’ function?
• Unreachable Switch Statement Default Cases
• __assume
• Is there a way to speed up a big switch statement?
• Most efficient method for large switch statements

2026-03-20

Interpreters:

• A Deep Dive into Dispatching Techniques | Jonathan Müller
• Parsing Protobuf at 2+GB/s: How I Learned To Love Tail Calls in C | Josh Haberman
• Subject: Re: Suggestions on implementing an efficient instruction set simulator in LuaJIT2
• The Implementation of Lua 5.0 (PDF, 145 KB)
• LuaJIT 2 beta 3 is out: Support both x32 & x64 (comment by mikemike)
• Template Interpreters | zackoverflow
• Cheaply writing a fast interpreter - Neil Mitchell
• Branch Prediction and the Performance of Interpreters - Don’t Trust Folklore
• Relative performance of interpreter implementations

2026-02-05

Misc:

• Establishing a Scalable Sparse Ecosystem with the Universal Sparse Tensor | Nvidia
• Tensor Algebra Processing Primitives (TAPP): Towards a Standard for Tensor Operations | arXiv
• I want a good parallel computer | Raph Levien
• Why didn’t Larrabee fail? | TomF’s Tech Blog
• Death and Life in Silicon Valley | Erik Lindholm (lecture recording)

Stencils:

• Compiling stencils in high performance Fortran
• Register Optimizations for Stencils on GPUs
• Evaluation of Programming Models and Performance for Stencil Computation on Current GPU Architectures

Batched BLAS:

• Batched BLAS | Innovative Computing Laboratory
• KokkosKernels: Compact Layouts for Batched Blas and Sparse Matrix-Matrix multiply

Runtime dispatch:

• The cost of runtime dispatch | Daniel Lemire
• Function Multiversioning | GCC
• ax, Qax (Compiler Option) | Intel oneAPI DPC++/C++ Compiler
• Function multi-versioning | MaskRay
• The - surprisingly limited - usefulness of function multiversioning in GCC
• Architecture Specific Code Generation and Function Multiversioning | Eric Christopher
• Generate code for multiple SIMD architectures
• Runtime CPU Detection | Nvidia Grace CPU Benchmarking Guide
• Intel® Intrinsics Guide
• CPU Dispatching: Make your code both portable and fast
• Instruction Set–Specific Dispatching on Intel® Architectures
• How does the CPU dispatcher work? | NumPy
• On CPU dispatch | AttractiveChaos

2026-01-12

Fortran Software Papers:

• Serious FORTRAN (1973)
• Serious FORTRAN—Part 2 (1973)
• Fortran 77 portability (1981)
• A fortran procedure for drawing some space-filling curves (1986)
• Module coupling and cohesion (COMP145, 2000) (see Team Software Engineering course homepage)
• Multibox Parsers (1994) (PDF, 702 KB)
• Multibox parsers: no more handwritten lexical parsers

Fortran Subsets (ELF90 and F):

• Loren Meissner, Little Giants - The New Fortran Subsets (1996) (PDF, 425 KB)
• Jerry Wagener, Fortran Reflections (1997)
• Fortran 90 subsets offer simplified environment for first-time users (1998)

Finite Difference Calculations:

• An extension of FORTRAN containing finite difference operators (1972)
• A Fortran program to generate finite difference formulas (1975)
• FORTRAN subprograms for finite-difference formulas (1976)
• Generation of finite difference formulas on arbitrarily spaced grids (1988) (formulas are implemented here, authors copy?)
• An abstract machine for partial differential equations (1994)
• Finite Difference Methods in CUDA Fortran, Part 1
• Finite Difference Methods in CUDA Fortran, Part 2

Make papers:

• Automatic generation of make dependencies (1984)
• A simple technique for automatic recompilation in modular programming languages (1989)
• Avoiding trickle-down recompilation in the Mary2 implementation

Misc:

• vpternlog: Signed Saturation
• Modal, GPU Glossary
• Fabien Sanglard, A History of Nvidia Stream Multiprocessor
• Heinz-Gerd Hegering, 50 Jahre LRZ (1962-2012)

2025-12-21

R history and other links:

• R: Past and Future History | Ross Ihaka (PDF, 92.5 KB)
• A Brief History of S | Richard A. Becker (PDF, 151 KB)
• History of S and R | John Chambers (PDF, 234 KB)
• History and Overview of R | R Programming for Data Science
• R behind the scenes: Using S the (un)usual way | Friedrich Leisch (PDF, 1357 KB)

(Gathered for my reply here.)

2025-12-20

Co-Array Fortran Resources:

• Coarray in the next Fortran Standard | John Reid
• Coarray Fortran on LRZ’s HPC systems
• Parallel programming with Fortran 2008 and 2018 coarrays | Anton Shterenlikht
• coarray-tutorial | Thomas König (look for tutorial.md)
• Introduction to PGAS | Cray Compiler Environment, HPE

Co-Array Fortran Articles:

• Writing a multigrid solver using co-array fortran (1998) | Numrich et al.
• A multi-platform co-array fortran compiler (2004) | Dotsenko et al.
• Co-arrays in the next Fortran standard (2005) | Numrich & Reid
• A parallel numerical library for co-array Fortran (2005) | Numrich
• Parallel numerical algorithms based on tensor notation and Co-Array Fortran syntax (2005) | Numrich
• A new vision for Coarray Fortran (2009) | Mellor-Crummey et al.
• An open-source compiler and runtime implementation for Coarray Fortran (2010) | Eachempati et al.
• A coarray fortran implementation to support data-intensive application development (2012) | Eachempati et al.
• Experiences at scale with PGAS versions of a Hydrodynamics application (2014) | Mallinson et al.
• Comparing Coarray Fortran (CAF) with MPI for several structured mesh PDE applications (2015) | Garain et al.
• MPI to Coarray Fortran: experiences with a CFD solver for unstructured meshes (2017) | Sharma & Moulitsas
• Accelerating Fortran codes: A method for integrating Coarray Fortran with CUDA Fortran and OpenMP (2025) | McKevitt et al.

Fortran debate:

• David Cann (1992). Retire Fortran? A Debate Rekindled (an earlier version is available in the Supercomputing ‘91 proceedings)
• Kuck & Wolfe (1984). A debate: Retire FORTRAN? No
• J.R. McGraw (1984) A debate: Retire FORTRAN? Yes

I found these at the bottom of the Fortran Wiki Articles page.

2025-12-18

Nvidia PTX Resources:

• Understanding PTX, the Assembly Language of CUDA GPU Computing | Nvidia
• Advanced NVIDIA CUDA Kernel Optimization Techniques: Handwritten PTX | Nvidia
• Inline PTX Assembly in CUDA | Nvidia
• What is Parallel Thread Execution? | Modal
• Tutorial: Understanding GPU Assembly with PTX | eunomia
• Parallel Thread Execution ISA Version 9.1 | Nvidia
• Parallel Thread Execution ISA Version 5.0 | Nvidia
• Parallel Thread Execution ISA Version 6.3 | Nvidia
• Parallel Thread Execution ISA Version 3.0 | Nvidia (PDF, 2.2 MB)
• PTX: Parallel Thread Execution ISA Version 1.4 (PDF, 2.5 MB)
• PTX: Parallel Thread Execution ISA Version 1.2 (PDF, 2.4 MB)
• PTX: Parallel Thread Execution ISA Version 1.0 (PDF, 558 KB)
• Nvidia’s PTX: Some background to the ‘virtual instruction set’ that underpins CUDA | The Chip Letter
• PTX Writer’s Guide to Interoperability | Nvidia
• A Gentle Introduction to CUDA PTX | Philip Fabianek (HN Thread)
• CUDA Binary Utilities | Nvidia
• CUDA PTX: GPU assembly language | Dr. Lawlor
• Maybe consider putting “cutlass” in your CUDA/Triton kernels | Henry Zhu
• ptx internals | redp
• Inside NVIDIA GPUs: Anatomy of high performance matmul kernels | Aleksa Gordić

2025-11-26

• Why Systolic Architectures? (PDF, 2.4 MB)
• Flang Documentation
• The Practitioner’s Cookbook for Good Parallel Performance on Multi- and Many-Core Systems | RRZE (PDF, 7.1 MB)
• Performance Analysis of the Apple AMX Matrix Accelerator | Jonathan Zhou (PDF, 1777 KB)
• Counting cycles and instructions on ARM-based Apple systems | Daniel Lemire
• Apple Firestorm/Icestorm CPU microarchitecture docs
• Finding and evaluating AMX co-processors in Apple silicon chips
• Apple vs. Oranges: Evaluating the Apple Silicon M-Series SoCs for HPC Performance and Efficiency
• A64 SIMD Instruction List: SVE Instructions
• High Performance Computing Class | FSU Jena
• Designing a SIMD Algorithm from Scratch | mcyoung

2025-11-20

• Comparing OpenBLAS and Accelerate on Apple Silicon for BLAS Routines | Frank Rosner
• Benchmarking and Testing | Tyler Sean Rau
• OpenMP* SIMD for Inclusive/Exclusive Scans
• Effiziente Nutzung von Hochleistungsrechnern in der numerischen Strömungsmechanik | Dr. Georg Hager (PDF, 599 KB)
• Automatic Translation of FORTRAN Programs to Vector Form | Randy Allen and Ken Kennedy (PDF, 2.8 MB)
• Multimedia vectorization of floating-point MIN/MAX reductions (2006) | Bik et al.
• Vectorization Essentials | Intel Software (PDF, 1913 KB)
• A comparison of 12 parallel Fortran dialects (1988) | Karp & Babb
• Extending MPI Correctness Benchmarking to the Fortran Language (2025) | Oraji et al.
• Investigating the performance of LLVM-based Intel Fortran Compiler (ifx) | Dhani Ruhela (PDF, 596 KB)
• Just Write Fortran: Experiences with a Language-Based Alternative to MPI+X (2024) | Rouson et al.

2025-11-19

• ButterflyPACK: Fast PDE solvers and transforms | NERSC