Nova: A Scalable Silicon Architecture for HPC, AI, and Cryptography

LupoToro’s silicon processor architecture designed to dramatically scale parallel computation for high-performance computing, artificial intelligence learning, and cryptographic workloads through novel matrix acceleration, memory hierarchy, and hardware partitioning concepts.

Modern GPUs (e.g. NVIDIA’s GeForce 8-series circa 2006–07) have evolved into highly parallel, unified processors that are increasingly used for scientific computing.  NVIDIA’s Tesla architecture (introduced with the GeForce 8800 in 2006) unifies vertex and pixel pipelines and exposes hundreds of cores to software via CUDA .  This “Nova” proposal builds on that trajectory.  Nova retains a scalable array of SIMD Streaming Multiprocessors (SMs) but adds HPC-oriented features.  In this paper we outline Nova’s design goals and features and flagging longer-term ideas as speculative.

Architecture Overview

Nova’s baseline resembles then-current Tesla GPUs: a hierarchy of SIMD multiprocessors feeding a coherent high-bandwidth memory system.  Key features include:

• Streaming Multiprocessors (SMs): Nova is built from many SMs, each containing multiple scalar ALUs and fast shared memory.  For example, the GeForce 8800 Ultra (a Tesla-based GPU) had 16 SMs with 128 total arithmetic cores.  Each SM in Nova supports fine-grained multithreading: it manages up to 768 concurrent threads in hardware with zero-overhead scheduling . Threads are organized into warps of ~32, following the SIMT (Single-Instruction-Multiple-Thread) model.  In aggregate, Nova can launch tens of thousands of threads to exploit massive parallelism.  (At 1.5 GHz, an 8800 Ultra SM delivered ~36 GFLOP/s of single-precision performance.)

• Memory Subsystem: Each Nova GPU includes high-speed GDDR DRAM and on-chip caches.  A late-2007 GPU example (8800 Ultra) paired 768 MB of GDDR3 at 1.08 GHz, yielding ~104 GB/s peak bandwidth .  Nova envisions similar or higher bandwidth, possibly with 512-bit or wider memory buses, to feed its many cores.  On-chip shared memory and caches help service the SMs.  As in existing GPUs, Nova provides fixed-function units (textures, rasterizers) for graphics, but we focus on the programmable pipeline for computation.

• PCI Express Interface & Multi-GPU: Nova connects to the host via PCI-Express (and potentially PCIe 2.0 as in 2007 GPUs ).  Its I/O fabric supports high throughput to and from host memory.  On a larger scale, Nova can be used in multi-GPU configurations: already in 2008 NVIDIA’s SLI (Scalable Link Interface) allowed multiple GPUs to cooperate.  In HPC clusters, Nova boards can be combined to scale out compute (e.g., NVLink did not yet exist, but multiple GPUs can share a host and memory hierarchy).

• Programmability: Nova continues the CUDA/C programming model of its era.  Any improvements (e.g. higher-level APIs or language features) would be incremental; the programming model remains SIMT and data-parallel.  Existing developer tools (debuggers, profilers) apply with minimal change.

• Scheduling and Partitioning (Speculative): Today’s GPUs are monolithic devices; Nova tentatively explores future resource sharing.  For example, one could partition a GPU’s SMs or time-slice them between user processes.  NVIDIA’s engineers note that improved scheduling and load-balancing are active research topics , and Nova might leverage these ideas.  We imagine “lightweight virtualization” where, say, half the SMs run one program and half run another, or the GPU switches context on a fine timeslice.  This is not present in 2008 hardware, but Nova highlights it as a research direction for multi-user HPC.

Potential Applications

Nova is aimed at scientific and technical computing workloads where throughput and energy efficiency matter.  Examples include:

• Dense Numerical Solvers: Nova excels at operations like dense linear algebra and FFTs, which map naturally to its SIMD cores.  GPUs in 2007 already sustain hundreds of GFLOP/s on such tasks; for instance, an 8800 Ultra achieves a theoretical peak of ~576 GFLOP/s .  Researchers have shown that regular, dense matrix–matrix multiplication (SGEMM) can effectively use GPU shared memory and caches.  Nova proposes adding even more hardware support (e.g. tiny “matrix multiply” units or scatter-gather accumulators) to accelerate small-block SGEMM kernels common in tile algorithms.  These would be integrated cautiously – perhaps as optional units controlled by software.  Overall, Nova targets HPC fields like fluid dynamics, molecular dynamics, and computational chemistry, where high-density floating-point work prevails.

• Sparse and Irregular Computations: Many scientific problems involve sparse matrices (e.g. finite-element methods, graph algorithms).  GPUs historically handle sparse data inefficiently due to indirect memory accesses.  CUDA research shows sparse matrix–vector multiply (SpMV) on GPUs is possible but “presents additional challenges”.  Nova recognizes this and incorporates modest features to help sparse codes: for example, faster scatter/gather instructions or improved caching for irregular patterns.  The goal is not a full-fledged specialized hardware (that would require deep co-design), but rather lighter hints (software-controlled prefetching or indexed load units) to improve throughput on sparse workloads without breaking the general-purpose SIMD model.

• Emerging AI/ML Workloads: By 2008, machine learning (e.g. neural nets for vision or pattern recognition) is still nascent, but shows promise for parallel hardware.  Nova speculates that over the next decade GPUs will increasingly be used for data mining and learning tasks.  Its high FLOP count and memory bandwidth bode well for, say, parallel convolution or large-batch matrix multiplies in neural nets.  (Nova does not claim built-in deep-learning units; such specialization was beyond 2008 knowledge.  Instead, we simply note that Nova’s general compute power naturally benefits many-core AI workloads.)

• Cryptography and Security: Cryptography tasks (encrypt/decrypt, hashing, brute-force key search) involve many identical small operations on different data – ideal for a parallel GPU.  In fact, early CUDA experiments confirm this: a homegrown CUDA program on an 8800 Ultra broke ~110 million MD5 hashes per second (~36× faster than a single-core CPU).  Meanwhile, GPUs of 2007 were already adding crypto engines: the 8800 GT included an on-chip AES-128 encryption/decryption block for HDCP protection .  Nova builds on these hints by envisioning optional support for common crypto primitives (e.g. AES rounds or SHA pipelines) in hardware, to accelerate secure communications or password cracking tasks in HPC (e.g. lattice cryptanalysis).  Such features would remain “opt-in” and programmable, and Nova would not abandon the CUDA model – it simply places crypto units alongside ALUs.

In all these domains, Nova balances immediate feasibility with long-term vision.  Near-term claims are grounded in data: GPUs deliver hundreds of GFLOP/s on dense math today , and have been successfully applied to linear algebra and graphics.  Speculative elements (like SM partitioning or matrix accelerators) are clearly labeled as research directions for the future.

Process Roadmap

Nova’s design assumes fabrication trends of the late-2000s.  In 2007–08, GPUs transitioned from 90 nm to 65 nm processes: for example, the 8800 GT (codename G92) was built on 65 nm with ~754 million transistors, up from 681M on the 90 nm G80 .  Industry roadmaps from 2006–07 (e.g. ATI/AMD/TSMC) projected 45 nm by ~2008 .  Accordingly, we expect Nova chips around 2009–10 to use 45 nm or 40 nm, with over a billion transistors.  Moore’s law scaling is assumed: roughly a 2× increase in transistor budget every 2 years allows Nova to double ALU count or memory width per generation.  NVIDIA’s own Tesla architects noted that “with future increases in transistor density, the architecture will readily scale [its] parallelism, memory partitions, and overall performance”.  Concretely, Nova’s roadmap might look like:

• 2008–2010 (45 nm era): GPUs adopt 45 nm, enabling ∼1–2 billion transistors.  Nova variants of this node could double SM count (e.g. 32 SMs instead of 16) and expand global memory to ~1–2 GB GDDR3/5.  Clock speeds might rise modestly (~1.5–2 GHz) or cores could run slower for power.  Additional memory channels (e.g. 6×64 bit) could push bandwidth >200 GB/s.  Performance on dense DGEMM/SGEMM could reach multi-TFLOPS per GPU.

• 2010–2012 (32/28 nm era): Shrinks allow >4–6 billion transistors.  Nova could introduce second-generation features: faster double-precision units for HPC, larger caches, and deeper pipelines.  Novel packaging (e.g. 3D-stacked memory) might also appear.  Applications like large-scale deep learning or simulations could run clusters of Nova GPUs at sustained multi-petaFLOP throughput.

• Beyond 2012: Continuing node shrinks (22/16 nm) suggest GPUs with 10+ billion transistors.  Nova as a concept is designed to scale: more SMs and memory banks, with expanded interconnect (for example, linking dozens of GPUs).  New domains could emerge (e.g. real-time ray tracing, advanced ML models) and Nova’s architecture is sufficiently general to incorporate them via software and minor hardware tweaks.

The roadmap remains hypothetical, but it is consistent with 2008 knowledge of process technology.  At each step, Nova’s core principles (many ALUs, wide memory, C-programmability) hold constant, while resources grow.

Nova is a forward-looking GPU architecture for HPC, pitched as if in 2008.  It preserves the proven unified-SM design of the Tesla generation while layering on speculative but plausible extensions (partitioning, matrix units, basic crypto support).  The design is careful to mark what is known from 2007 hardware versus what is projected, so that a well-informed 2008 audience can see Nova as credible yet ambitious.