mhtirogla
Home
Systems
API
Pricing
About
Log in
← Back
Home
›
Systems
›
Simulation and scientific computing
plasma-fusion-sim
plasma turbulence and fusion simulation
GENE, BOUT++
System
Techniques · 9
Primitives · 46
plasma-fusion-sim
Batch checkpoint restart
distributed · medium: Large batch jobs (end-of-day, settlement, payroll runs) that checkpoint and restart idempotently
Halo exchange
distributed · medium: Each rank stores its block plus a border copy of its neighbours' edge cells, refreshed by small messages every step; communication scales with block surface, not volume.
›
Parallel file system striping
distributed · medium: Striping files across object storage targets (Lustre, GPFS); collective I/O and metadata servers
Kernel autotuning
ml-training · medium: Searching tile sizes and launch parameters per shape/hardware, caching the best configs
Gmres
numerics · medium: GMRES with restarts: Arnoldi, Givens rotations, for nonsymmetric sparse systems
Chunked array storage
storage · medium: Cut an N-dimensional array into a regular grid of independently compressed blocks, so a slice read touches only the overlapping blocks and writers to different blocks run in parallel.
›
Incomplete factorization preconditi…
numerics · medium: Sparse approximate LU or Cholesky factors (ILU, IC) that precondition Krylov solvers: fewer iterations than Jacobi, less memory than a direct solve.
›
Gpu memory coalescing banking
hardware-os · low: Coalesced global loads, shared memory bank conflicts, L2 and HBM bandwidth on GPUs
CUDA and GPU languages
languages · low: Programming GPUs: thousands of lockstep threads, bound by memory bandwidth; libraries first, Triton for fused kernels, CUDA C++ for peak speed.
›
Collective communication
ml-training · low: The family of group exchanges (broadcast, reduce, all-gather, reduce-scatter, all-to-all) and the latency-plus-bytes cost model that picks ring, tree or direct algorithms by message size and topology.
›
Tensor layout transforms
ml-training · low: Choosing and converting tensor layouts (NCHW/NHWC, blocked, tiled) for kernel efficiency
Adaptive Runge-Kutta (Dormand-Princ…
numerics · low: An embedded 5(4) Runge-Kutta pair estimates each step's error for free and resizes the step to meet a tolerance; the default non-stiff solver.
›
Fast Fourier transform
numerics · low: Computes the DFT in O(n log n) by recursively splitting into even and odd halves; the basis of fast convolution and spectral methods.
›
Finite differences
numerics · low: Replace derivatives with weighted differences of grid values; simple stencils plus a stability condition (CFL) turn PDEs like heat, advection and waves into array loops.
›
Monte Carlo integration
numerics · low: Estimate an expectation by averaging random samples; error falls as σ/√N in any dimension, and variance reduction (antithetic, control variates, importance sampling) shrinks σ.
›
Multigrid
numerics · low: Smooths high-frequency error on the fine grid and solves the smooth remainder on coarser grids; O(n) elliptic solves at any resolution.
›
Runge-Kutta 4
numerics · low: The classic four-stage, fourth-order ODE step; error drops 16× when the step halves, with no derivatives or past steps needed.
›
Spectral methods
numerics · low: Represent the solution by a global Fourier or Chebyshev series; derivatives become multiplications by ik in coefficient space, giving exponential convergence for smooth problems.
›
Stiff solvers (implicit, BDF)
numerics · low: Implicit steps solve a small nonlinear system so fast decaying modes stay stable at any step size; BDF methods are the stiff-ODE workhorse.
›
Idempotency keys
finance · low: Client-chosen unique key per logical operation, stored with a request fingerprint and the final response, so retries replay the first result instead of acting twice.
›
Write-ahead log
storage · low: Append each change to a checksummed sequential log and fsync it before touching data pages; the basis of crash durability in nearly every database.
›
Cron scheduling
systems · low: Cron expression parsing and next-fire-time computation, with catch-up and time-zone rules
Eager rendezvous protocol
distributed · low: MPI point-to-point: eager sends for small messages, rendezvous handshake for large ones
+ 2 more techniques
+ 30 more primitives
System
Techniques · 9
Primitives · 46
plasma-fusion-sim
Batch checkpoint restart
distributed · medium: Large batch jobs (end-of-day, settlement, payroll runs) that checkpoint and restart idempotently
Halo exchange
distributed · medium: Each rank stores its block plus a border copy of its neighbours' edge cells, refreshed by small messages every step; communication scales with block surface, not volume.
›
Parallel file system striping
distributed · medium: Striping files across object storage targets (Lustre, GPFS); collective I/O and metadata servers
Kernel autotuning
ml-training · medium: Searching tile sizes and launch parameters per shape/hardware, caching the best configs
Gmres
numerics · medium: GMRES with restarts: Arnoldi, Givens rotations, for nonsymmetric sparse systems
Chunked array storage
storage · medium: Cut an N-dimensional array into a regular grid of independently compressed blocks, so a slice read touches only the overlapping blocks and writers to different blocks run in parallel.
›
Incomplete factorization preconditi…
numerics · medium: Sparse approximate LU or Cholesky factors (ILU, IC) that precondition Krylov solvers: fewer iterations than Jacobi, less memory than a direct solve.
›
Fill-reducing ordering
numerics · medium: Permute a sparse symmetric matrix before Cholesky/LU so the factor has few new nonzeros; minimum degree, nested dissection and RCM trade quality, speed and parallelism.
›
Graph partitioning
graphs · medium: Split a graph into k balanced parts with few edges between them; multilevel coarsen-partition-refine (METIS, KaHIP) for quality, streaming heuristics (LDG, Fennel) for one-pass scale.
›
Gpu memory coalescing banking
hardware-os · low: Coalesced global loads, shared memory bank conflicts, L2 and HBM bandwidth on GPUs
CUDA and GPU languages
languages · low: Programming GPUs: thousands of lockstep threads, bound by memory bandwidth; libraries first, Triton for fused kernels, CUDA C++ for peak speed.
›
Collective communication
ml-training · low: The family of group exchanges (broadcast, reduce, all-gather, reduce-scatter, all-to-all) and the latency-plus-bytes cost model that picks ring, tree or direct algorithms by message size and topology.
›
Tensor layout transforms
ml-training · low: Choosing and converting tensor layouts (NCHW/NHWC, blocked, tiled) for kernel efficiency
Adaptive Runge-Kutta (Dormand-Princ…
numerics · low: An embedded 5(4) Runge-Kutta pair estimates each step's error for free and resizes the step to meet a tolerance; the default non-stiff solver.
›
Fast Fourier transform
numerics · low: Computes the DFT in O(n log n) by recursively splitting into even and odd halves; the basis of fast convolution and spectral methods.
›
Finite differences
numerics · low: Replace derivatives with weighted differences of grid values; simple stencils plus a stability condition (CFL) turn PDEs like heat, advection and waves into array loops.
›
Monte Carlo integration
numerics · low: Estimate an expectation by averaging random samples; error falls as σ/√N in any dimension, and variance reduction (antithetic, control variates, importance sampling) shrinks σ.
›
Multigrid
numerics · low: Smooths high-frequency error on the fine grid and solves the smooth remainder on coarser grids; O(n) elliptic solves at any resolution.
›
Runge-Kutta 4
numerics · low: The classic four-stage, fourth-order ODE step; error drops 16× when the step halves, with no derivatives or past steps needed.
›
Spectral methods
numerics · low: Represent the solution by a global Fourier or Chebyshev series; derivatives become multiplications by ik in coefficient space, giving exponential convergence for smooth problems.
›
Stiff solvers (implicit, BDF)
numerics · low: Implicit steps solve a small nonlinear system so fast decaying modes stay stable at any step size; BDF methods are the stiff-ODE workhorse.
›
Idempotency keys
finance · low: Client-chosen unique key per logical operation, stored with a request fingerprint and the final response, so retries replay the first result instead of acting twice.
›
Write-ahead log
storage · low: Append each change to a checksummed sequential log and fsync it before touching data pages; the basis of crash durability in nearly every database.
›
Cron scheduling
systems · low: Cron expression parsing and next-fire-time computation, with catch-up and time-zone rules
Eager rendezvous protocol
distributed · low: MPI point-to-point: eager sends for small messages, rendezvous handshake for large ones
Rdma
networking · low: RDMA verbs over InfiniBand/RoCE: queue pairs, memory registration, one-sided ops
Zero-copy I/O
networking · low: Move bytes between files, sockets and NICs by passing page references instead of copying through user space: sendfile, splice, MSG_ZEROCOPY, kTLS.
›
Leases
distributed · low: A lock that expires on its own: bounded-time ownership that survives crashes and partitions, given bounded clock drift and fencing tokens on writes.
›
Range partitioning
distributed · low: Cut the sorted key space into contiguous half-open ranges kept in a boundary map; split hot or big ranges, merge small ones, move ranges between nodes.
›
Roofline model
hardware-os · low: Bound a kernel's speed by min(peak FLOP/s, arithmetic intensity × memory bandwidth) to know whether to optimize compute or data movement.
›
GPU compute shaders
ml-training · low: The vendor-neutral GPU launch model of Metal, Vulkan and WebGPU: a grid of workgroups, shared memory, barriers and subgroups, driven by recorded command buffers.
›
Tiled matrix multiplication
ml-training · low: Block GEMM into tiles held in shared memory and registers, so each byte loaded from DRAM feeds many FMAs; the core of fast GEMM.
›
Robust statistics
statistics · low: Location and scale estimates (median, MAD, trimmed mean, Huber M-estimator) that stay correct when a minority of the data is garbage.
›
Sparse matrices (CSR)
numerics · low: Compressed sparse row stores only nonzeros as row pointers, column indices and values; SpMV streams them once, so it is memory-bandwidth bound.
›
Object storage
cloud · low: S3-compatible blob storage at cents per GB-month with 11 nines durability; pick by egress price, request price and latency, not by storage price alone.
›
Delta encoding
compression · low: Store differences between consecutive values instead of the values, turning sorted IDs, timestamps and counters into small numbers that pack into few bits.
›
Zstandard (zstd)
compression · low: LZ77 plus Huffman and FSE entropy coding, with levels from LZ4-like speed to xz-like ratio, all decoding at about 1-2 GB/s.
›
HTTP range requests
networking · low: Ask for byte offsets with Range and get a 206 slice back; the standard way to resume, seek and read big remote files piecewise.
›
LU and Cholesky factorization
numerics · low: Factor A = PLU (any square matrix, partial pivoting) or A = LLᵀ (SPD) once in O(n³), then solve each right-hand side in O(n²).
›
d-ary heap
data-structures · low: Array heap with d children per node (d = 4 typical): half the binary heap's depth, siblings in one cache line, faster priority queues.
›
PRNGs (PCG, xoshiro)
numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.
›
Bit packing and PFOR
compression · low: Store blocks of integers with exactly b bits each and patch rare outliers separately (PFOR); the fastest integer codec family, billions of values per second.
›
Varint (LEB128)
compression · low: Variable-length integers in 7-bit groups with a continuation bit; small numbers take 1 byte, the wire format of Protobuf, WebAssembly and DWARF.
›
Fencing tokens
distributed · low: A number that grows with every lock grant and is checked by the protected resource, so a paused ex-holder's late write is rejected.
›
CPU caches and the memory hierarchy
hardware-os · low: Sizes, line widths and latencies of L1/L2/L3/DRAM on modern x86 and Apple M-series, and the data-layout rules that follow from them.
›
CRC32C checksums
hardware-os · low: The Castagnoli CRC-32 with dedicated instructions on x86 (SSE4.2) and ARMv8. It is the standard fast integrity check for storage blocks, logs and network frames.
›
fsync, O_DSYNC and O_DIRECT
hardware-os · low: What fsync, fdatasync, O_DSYNC, O_DIRECT and F_FULLFSYNC really guarantee on Linux and macOS, and the exact call sequences for crash-safe files.
›
SIMD programming
hardware-os · low: Process 4-16 lanes per instruction with AVX2, AVX-512 or NEON via Rust std::arch, with runtime dispatch and a portable fallback.
›
Rejection sampling
ml-training · low: Best-of-n and filtered sampling: generate many candidates, keep those passing a verifier or reward threshold
Discrete cosine transform
numerics · low: Fast integer DCT/DST block transforms used by image and video codecs
Explicit Euler
numerics · low: Step an ODE forward by its current slope, y += h·f(t, y); first-order, one evaluation per step, the baseline every other integrator is measured against.
›
Newton root finding
numerics · low: Linearize at the guess and jump to the tangent root; quadratic convergence near a simple root, made robust by brackets (1D) or line search.
›
Thomas tridiagonal solver
numerics · low: O(n) tridiagonal linear solve used in implicit finite-difference and spline schemes
Cryptographic hashes (SHA-2, BLAKE3)
security · low: Collision-resistant fingerprints of data; SHA-256 for interoperability, BLAKE3 for speed, HMAC/HKDF for keyed uses.
›
Exponential backoff with jitter
systems · low: Retry after delays that grow geometrically and are randomized, so failed clients spread out instead of hammering a recovering service in synchronized waves.
›
Copy for AI
Similar systems
Share the most building blocks with this one.
les-dns-solver
52 %
seismic-imaging
38 %
lattice-qcd
31 %
earthquake-sim
27 %
electromagnetics-sim
26 %